CN114943582A - Information recommendation method and system and recommendation server - Google Patents

Information recommendation method and system and recommendation server Download PDF

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CN114943582A
CN114943582A CN202210524498.6A CN202210524498A CN114943582A CN 114943582 A CN114943582 A CN 114943582A CN 202210524498 A CN202210524498 A CN 202210524498A CN 114943582 A CN114943582 A CN 114943582A
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刘军
张赫麟
肖钢
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China Securities Co Ltd
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Abstract

The embodiment of the invention provides an information recommendation method, an information recommendation system and a recommendation server. The recommendation server obtains a plurality of investment strategies to be recommended; and obtaining the user characteristics of the target user and the product characteristics corresponding to each investment strategy to be recommended from the trading server. The recommendation server calculates the similarity between each investment strategy to be recommended and the target user; and obtaining candidate investment strategies with the similarity meeting preset selection conditions, and sending an investment evaluation value acquisition request of each candidate investment strategy to the trading server. And the transaction server calculates a target investment evaluation value of the candidate investment strategy based on the transaction information corresponding to the candidate investment strategy and returns the target investment evaluation value to the recommendation server. The recommendation server determines a target investment strategy from the candidate investment strategies; and sending the target investment strategy as recommendation information to the client. The client displays the recommendation information, and can provide personalized recommendation service for each user, so that the information recommendation efficiency is improved.

Description

Information recommendation method and system and recommendation server
Technical Field
The invention relates to the technical field of big data processing, in particular to an information recommendation method, an information recommendation system and a recommendation server.
Background
Currently, big data processing technology has been applied to various fields. One important application of big data processing technology in various fields is to generate recommendation information of corresponding fields based on a large amount of historical data and output the recommendation information to users. For example: the video system can generate a recommended video based on the historical data and output the recommended video to the user; and the following steps: in the field of electronic commerce, the generation of recommendation information of commodities and the output of the recommendation information to users and the like are realized based on historical data.
However, large data processing techniques have not been particularly widely used in the financial field. For example: when investment strategy recommendation is carried out, still, quantitative analysis is carried out on historical data manually by professionals so as to obtain target investment strategies to be recommended to users. It can be seen that this method requires a large labor cost, and cannot provide a personalized recommendation service for each user.
Disclosure of Invention
The embodiment of the invention aims to provide an information recommendation method, an information recommendation system and a recommendation server, so as to provide personalized recommendation service for each user and improve information recommendation efficiency. The specific technical scheme is as follows:
in a first aspect, to achieve the above object, an embodiment of the present invention provides an information recommendation method, where the method is applied to a recommendation server in a recommendation system, and the recommendation system further includes a client; the recommendation server is in communication connection with the transaction server, and the method comprises the following steps:
receiving an information recommendation request sent by the client; the information recommendation request comprises a target user identifier and configuration information of information to be recommended; under the condition that the information to be recommended is determined to be the investment strategy based on the configuration information, obtaining a plurality of investment strategies to be recommended based on the configuration information; sending a corresponding user characteristic acquisition request to the transaction server based on the target user identification; so that the trading server obtains the user characteristics of the target user based on the historical investment data of the target user and returns the user characteristics to the recommendation server; sending a corresponding product characteristic acquisition request to the trading server aiming at each investment strategy to be recommended; so that the trading server obtains the product characteristics of the investment product corresponding to the investment strategy to be recommended and returns the product characteristics to the recommending server; respectively calculating the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user, and taking the similarity as the similarity between the investment strategy to be recommended and the target user; acquiring an investment strategy to be recommended, of which the similarity with the target user meets a preset selection condition, and taking the investment strategy as a candidate investment strategy; sending a corresponding investment evaluation value acquisition request to the trading server aiming at each candidate investment strategy; the transaction server obtains historical transaction scene information in a first historical time period according to the investment evaluation value obtaining request, and calculates first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server; determining a target investment strategy of which the corresponding target investment evaluation value meets a preset recommendation condition from the candidate investment strategies; and sending the target investment strategy as recommendation information to the client so as to enable the client to display the recommendation information.
Optionally, the configuration information includes: configuration parameter values for determining policy configuration items of the investment policy; the configuration parameter value is input by the target user through a configuration page; a plurality of policy configuration items are displayed in the configuration page;
the obtaining a plurality of investment strategies to be recommended based on the configuration information under the condition that the information to be recommended is determined to be the investment strategy based on the configuration information comprises the following steps: generating a plurality of investment strategies to be recommended based on configuration parameter values of the strategy configuration items input by the target user; wherein, each investment strategy to be recommended comprises a screening condition and a trading mode.
Optionally, the configuration information further includes: the target user determines a plurality of target evaluation indexes through the configuration page; the investment evaluation value acquisition request comprises a plurality of target evaluation indexes, so that the trading server determines a target investment evaluation value of the candidate investment strategy based on the target evaluation indexes, the first trading information and the second trading information;
alternatively, the first and second liquid crystal display panels may be,
the investment evaluation value acquisition request comprises the target user identification, so that the transaction server determines an evaluation index matched with the user characteristic of the target user from preset evaluation indexes to serve as a target evaluation index; and determining the target investment evaluation value of the candidate investment strategy based on the target evaluation index, the first transaction information and the second transaction information.
Optionally, the configuration information includes: strategy identification of investment strategy;
the obtaining a plurality of investment strategies to be recommended based on the configuration information under the condition that the information to be recommended is determined to be the investment strategy based on the configuration information comprises the following steps: determining an investment strategy to which the strategy identification belongs from all investment strategies recorded locally to obtain a plurality of investment strategies to be recommended; wherein, each investment strategy to be recommended comprises a screening condition and a trading mode.
Optionally, the calculating the similarity between the product characteristic corresponding to each investment strategy to be recommended and the user characteristic of the target user respectively as the similarity between the investment strategy to be recommended and the target user includes: aiming at each investment strategy to be recommended, mapping the product characteristics corresponding to the investment strategy to be recommended to obtain a first characteristic vector corresponding to the investment strategy to be recommended; mapping the user characteristics of the target user to obtain a second characteristic vector corresponding to the target user; calculating the similarity between the first feature vector and the second feature vector to obtain the similarity between the product features corresponding to the investment strategy to be recommended and the user features of the target user, and taking the similarity as the similarity between the investment strategy to be recommended and the target user;
the step of obtaining the investment strategy to be recommended with the similarity to the target user meeting the preset selection condition as a candidate investment strategy comprises the following steps:
determining a preset number of investment strategies to be recommended from the investment strategies to be recommended as candidate investment strategies according to the sequence of similarity from the target user to the target user from large to small;
alternatively, the first and second electrodes may be,
and determining the investment strategies to be recommended with the similarity greater than a preset threshold value with the target user from the investment strategies to be recommended as candidate investment strategies.
In a second aspect, in order to achieve the above object, an embodiment of the present invention provides an information recommendation method, where the method is applied to a trading server, and the trading server is in communication connection with a recommendation server in a recommendation system; the recommendation system further comprises a client; the method comprises the following steps:
when a user characteristic obtaining request sent by the recommendation server based on a target user identifier contained in an information recommendation request is received, obtaining the user characteristics of a target user based on historical investment data of the target user, and returning the user characteristics to the recommendation server; the information recommendation request is sent to the recommendation server by a client, and comprises a target user identifier and configuration information of information to be recommended; for each investment strategy to be recommended, when a product characteristic acquisition request sent by the recommendation server is received, acquiring the product characteristics of the investment product corresponding to the investment strategy to be recommended, and returning the product characteristics to the recommendation server; the recommendation server respectively calculates the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user, and the similarity is used as the similarity between the investment strategy to be recommended and the target user; acquiring an investment strategy to be recommended, of which the similarity with the target user meets a preset selection condition, and taking the investment strategy as a candidate investment strategy; wherein, the investment strategy to be recommended is as follows: the recommendation server is obtained based on the configuration information under the condition that the information to be recommended is determined to be an investment strategy based on the configuration information; for each candidate investment strategy, when an investment evaluation value acquisition request sent by the recommendation server is received, acquiring historical transaction scene information in a first historical time period for the investment evaluation value acquisition request, and calculating first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server; so that the recommendation server determines a target investment strategy, of which the corresponding target investment evaluation value meets preset recommendation conditions, from the candidate investment strategies; and sending the target investment strategy as recommendation information to the client so as to enable the client to display the recommendation information.
Optionally, the obtaining historical trading scenario information in a first historical time period, and calculating first trading information corresponding to the candidate investment strategy based on the historical trading scenario information includes: acquiring product information of each investment product in a historical trading scene in the first historical time period as first product information; determining each investment product matched with the screening condition in the candidate investment strategy from the investment products in the historical trading scene in the first historical time period as a first investment product; calculating transaction information which accords with the target evaluation index when investment is carried out according to the candidate investment strategy in the first historical time period based on the transaction mode in the candidate investment strategy and the first product information of the first investment product, and taking the transaction information as first transaction information;
the obtaining of the second transaction information corresponding to the second historical time period corresponding to the candidate investment strategy includes: acquiring product information of each investment product in a historical trading scene in the second historical time period as second product information; determining each investment product matched with the screening condition in the candidate investment strategy from each investment product in the historical trading scene in the second historical time period as a second investment product; and in the second historical time period, performing simulated transaction based on the transaction mode in the candidate investment strategy and the second product information of the second investment product to obtain transaction information meeting the target evaluation index as second transaction information.
Optionally, the investment evaluation value acquisition request carries a plurality of target evaluation indexes;
the calculating the target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information comprises: aiming at each target evaluation index, calculating an index value of the target evaluation index as a first index value based on first transaction information conforming to the target evaluation index; calculating a weighted sum of first index values of the target evaluation indexes as a first investment evaluation value; calculating an index value of the target evaluation index as a second index value based on the second transaction information meeting the target evaluation index; calculating the weighted sum of the second index values of the target evaluation indexes to serve as a second investment evaluation value; and calculating the weighted sum of the first investment evaluation value and the second investment evaluation value to obtain the target investment evaluation value of the candidate investment strategy.
In a third aspect, to achieve the above object, an embodiment of the present invention provides an information recommendation system, where the recommendation system includes: a client and a recommendation server; the recommendation server is in communication connection with the transaction server;
the client is used for sending an information recommendation request containing configuration information of a target user identifier and information to be recommended to the recommendation server; the recommendation server is used for receiving the information recommendation request; under the condition that the information to be recommended is determined to be the investment strategy based on the configuration information, obtaining a plurality of investment strategies to be recommended based on the configuration information; sending a corresponding user characteristic acquisition request to the transaction server based on the target user identification; the transaction server is used for obtaining the user characteristics of the target user based on the historical investment data of the target user and returning the user characteristics to the recommendation server; the recommendation server is also used for sending a corresponding product characteristic acquisition request to the trading server aiming at each investment strategy to be recommended; the transaction server is also used for obtaining the product characteristics of the investment product corresponding to the investment strategy to be recommended and returning the product characteristics to the recommendation server; the recommendation server is further configured to calculate similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user respectively, and the similarity is used as the similarity between the investment strategy to be recommended and the target user; acquiring an investment strategy to be recommended, of which the similarity with the target user meets a preset selection condition, and taking the investment strategy as a candidate investment strategy; sending a corresponding investment evaluation value acquisition request to the trading server aiming at each candidate investment strategy; the transaction server is further used for obtaining historical transaction scene information in a first historical time period according to the investment evaluation value obtaining request, and calculating first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and acquiring second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server; the recommendation server is further used for determining a target investment strategy of which the corresponding target investment evaluation value meets preset recommendation conditions from the candidate investment strategies; sending the target investment strategy as recommendation information to the client; the client is further used for displaying the recommendation information.
In a fourth aspect, to achieve the above object, an embodiment of the present invention provides a recommendation server, where the recommendation server includes:
the information recommendation request receiving module is used for receiving an information recommendation request sent by the client; the information recommendation request comprises a target user identifier and configuration information of information to be recommended; the to-be-recommended investment strategy acquisition module is used for acquiring a plurality of to-be-recommended investment strategies based on the configuration information under the condition that the to-be-recommended information is determined to be an investment strategy based on the configuration information; the user characteristic acquisition module is used for sending a corresponding user characteristic acquisition request to the transaction server based on the target user identification; so that the transaction server obtains the user characteristics of the target user based on the transaction data of the historical investment of the target user and returns the user characteristics to the recommendation server; the product characteristic acquisition module is used for sending a corresponding product characteristic acquisition request to the trading server aiming at each investment strategy to be recommended; so that the trading server obtains the product characteristics of the investment product corresponding to the investment strategy to be recommended and returns the product characteristics to the recommending server; the similarity calculation module is used for calculating the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user respectively, and the similarity is used as the similarity between the investment strategy to be recommended and the target user; the candidate investment strategy acquisition module is used for acquiring an investment strategy to be recommended, the similarity of which with the target user meets a preset selection condition, and the investment strategy to be recommended is used as a candidate investment strategy; the investment evaluation value acquisition module is used for sending a corresponding investment evaluation value acquisition request to the trading server aiming at each candidate investment strategy; the transaction server obtains historical transaction scene information in a first historical time period according to the investment evaluation value obtaining request, and calculates first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server; the target investment strategy acquisition module is used for determining a target investment strategy of which the corresponding target investment evaluation value meets a preset recommendation condition from the candidate investment strategies; and the recommendation information output module is used for sending the target investment strategy as recommendation information to the client so as to enable the client to display the recommendation information.
Optionally, the configuration information includes: configuration parameter values for determining policy configuration items of the investment policy; the configuration parameter value is input by the target user through a configuration page; a plurality of policy configuration items are displayed in the configuration page;
the investment strategy to be recommended acquisition module is specifically used for generating a plurality of investment strategies to be recommended based on the configuration parameter values of the strategy configuration items input by the target user; wherein, each investment strategy to be recommended comprises a screening condition and a trading mode.
Optionally, the configuration information further includes: the target user determines a plurality of target evaluation indexes through the configuration page; the investment evaluation value acquisition request comprises a plurality of target evaluation indexes, so that the trading server determines a target investment evaluation value of the candidate investment strategy based on the target evaluation indexes, the first trading information and the second trading information;
alternatively, the first and second electrodes may be,
the investment evaluation value acquisition request comprises the target user identification, so that the transaction server determines an evaluation index matched with the user characteristic of the target user from preset evaluation indexes to serve as a target evaluation index; and determining the target investment evaluation value of the candidate investment strategy based on the target evaluation index, the first transaction information and the second transaction information.
Optionally, the configuration information includes: strategy identification of investment strategy;
the investment strategy to be recommended acquisition module is specifically used for determining the investment strategy to which the strategy identifier belongs from the investment strategies recorded locally to obtain a plurality of investment strategies to be recommended; wherein, each investment strategy to be recommended comprises a screening condition and a trading mode.
Optionally, the similarity calculation module is specifically configured to, for each investment strategy to be recommended, perform mapping processing on product features corresponding to the investment strategy to be recommended to obtain a first feature vector corresponding to the investment strategy to be recommended; mapping the user characteristics of the target user to obtain a second characteristic vector corresponding to the target user; calculating the similarity between the first feature vector and the second feature vector to obtain the similarity between the product features corresponding to the investment strategy to be recommended and the user features of the target user, and taking the similarity as the similarity between the investment strategy to be recommended and the target user;
the candidate investment strategy acquisition module is specifically used for determining a preset number of investment strategies to be recommended from the investment strategies to be recommended as candidate investment strategies according to the sequence of similarity from the target user to the target user from large to small;
alternatively, the first and second electrodes may be,
and determining the investment strategies to be recommended with the similarity greater than a preset threshold value with the target user from the investment strategies to be recommended as candidate investment strategies.
In a fifth aspect, to achieve the above object, an embodiment of the present invention provides a transaction server, including:
the user characteristic acquisition module is used for acquiring the user characteristics of the target user based on historical investment data of the target user when receiving a user characteristic acquisition request sent by the recommendation server based on the target user identifier contained in the information recommendation request, and returning the user characteristics to the recommendation server; the information recommendation request is sent to the recommendation server by a client, and comprises a target user identifier and configuration information of information to be recommended; the product characteristic acquisition module is used for acquiring the product characteristics of the investment product corresponding to each investment strategy to be recommended when receiving a product characteristic acquisition request sent by the recommendation server and returning the product characteristics to the recommendation server; the recommendation server respectively calculates the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user, and the similarity is used as the similarity between the investment strategy to be recommended and the target user; acquiring an investment strategy to be recommended, of which the similarity with the target user meets a preset selection condition, and taking the investment strategy as a candidate investment strategy; wherein, the investment strategy to be recommended is as follows: the recommendation server is obtained based on the configuration information under the condition that the information to be recommended is determined to be an investment strategy based on the configuration information; the transaction information acquisition module is used for acquiring historical transaction scene information in a first historical time period according to the investment evaluation value acquisition request when the investment evaluation value acquisition request sent by the recommendation server is received aiming at each candidate investment strategy, and calculating first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; the investment evaluation value acquisition module is used for calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server; so that the recommendation server determines a target investment strategy, of which the corresponding target investment evaluation value meets preset recommendation conditions, from the candidate investment strategies; and sending the target investment strategy as recommendation information to the client so as to enable the client to display the recommendation information.
Optionally, the transaction information obtaining module is specifically configured to obtain product information of each investment product in a historical transaction scenario within the first historical time period, as first product information; determining each investment product matched with the screening condition in the candidate investment strategy from the investment products in the historical trading scene in the first historical time period as a first investment product; calculating transaction information which accords with the target evaluation index when investment is carried out according to the candidate investment strategy in the first historical time period based on the transaction mode in the candidate investment strategy and the first product information of the first investment product, and taking the transaction information as first transaction information;
the transaction information acquisition module is specifically used for acquiring product information of each investment product in a historical transaction scene in the second historical time period as second product information; determining each investment product matched with the screening condition in the candidate investment strategy from each investment product in the historical trading scene in the second historical time period as a second investment product; and in the second historical time period, performing simulated transaction based on the transaction mode in the candidate investment strategy and the second product information of the second investment product to obtain transaction information which meets the target evaluation index and is used as second transaction information.
Optionally, the investment evaluation value acquisition request carries a plurality of target evaluation indexes;
the investment evaluation value acquisition module is specifically used for calculating an index value of each target evaluation index as a first index value based on first transaction information conforming to the target evaluation index; calculating a weighted sum of first index values of the target evaluation indexes as a first investment evaluation value; calculating an index value of the target evaluation index as a second index value based on the second transaction information conforming to the target evaluation index; calculating the weighted sum of the second index values of the target evaluation indexes to serve as a second investment evaluation value; and calculating the weighted sum of the first investment evaluation value and the second investment evaluation value to obtain the target investment evaluation value of the candidate investment strategy.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the information recommendation method according to the first aspect or the second aspect.
An embodiment of the present invention further provides a computer program product including instructions, which when run on a computer, causes the computer to execute the information recommendation method according to any one of the first aspect or the second aspect.
According to the technical scheme provided by the embodiment of the invention, the client sends the information recommendation request containing the target user identification and the configuration information of the information to be recommended to the recommendation server. The recommendation server obtains a plurality of investment strategies to be recommended based on the configuration information under the condition that the information to be recommended is determined to be the investment strategies based on the configuration information in the received information recommendation request; and sending a corresponding user characteristic acquisition request to the transaction server based on the target user identification. And the transaction server obtains the user characteristics of the target user and returns the user characteristics to the recommendation server based on the historical investment data of the target user. And the recommending server sends a corresponding product characteristic obtaining request to the trading server aiming at each investment strategy to be recommended. And the trading server obtains the product characteristics of the investment product corresponding to the investment strategy to be recommended and returns the product characteristics to the recommending server. The recommendation server respectively calculates the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user, and the similarity is used as the similarity between the investment strategy to be recommended and the target user; acquiring an investment strategy to be recommended, of which the similarity with a target user meets a preset selection condition, and taking the investment strategy as a candidate investment strategy; and sending a corresponding investment evaluation value acquisition request to the trading server aiming at each candidate investment strategy. And the transaction server acquires historical transaction scene information in a first historical time period according to the investment evaluation value acquisition request, and calculates first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information. The first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; the second historical time period is a time period for carrying out simulated transaction based on the candidate investment strategy; and calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning to the recommendation server. The recommendation server determines a target investment strategy of which the corresponding target investment evaluation value meets preset recommendation conditions from the candidate investment strategies; and sending the target investment strategy as recommendation information to the client. And the client displays the recommendation information.
Based on the processing, based on the user characteristics of the target user, candidate investment strategies with the similarity degree with the user characteristics of the user meeting the preset selection conditions are determined, the target investment strategies recommended to the user are determined from the candidate investment strategies, the target investment strategies are the investment strategies meeting the real requirements of the target user, personalized recommendation service can be provided for the target user, the candidate investment strategies are determined from the investment strategies to be recommended, the data volume of follow-up processing can be reduced, and the information recommendation efficiency can be improved.
Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by referring to these drawings.
Fig. 1 is a structural diagram of an information recommendation system according to an embodiment of the present invention;
fig. 2 is a flowchart of an information recommendation method according to an embodiment of the present invention;
FIG. 3 is a flowchart of another information recommendation method according to an embodiment of the present invention;
FIG. 4 is a detailed flow chart of obtaining transaction information according to an embodiment of the invention;
FIG. 5 is a flowchart illustrating the steps of determining investment valuation according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating a configuration page displayed by a client in an embodiment of the invention;
FIG. 7 is a schematic diagram illustrating an information recommendation method according to an embodiment of the present invention;
fig. 8 is a structural diagram of a recommendation server according to an embodiment of the present invention;
fig. 9 is a block diagram of a transaction server according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived from the embodiments given herein by one of ordinary skill in the art, are within the scope of the invention.
In the related art, the big data processing technology has not been widely applied in the financial field. For example: when investment strategy recommendation is carried out, quantitative analysis is still carried out on historical data manually by professionals so as to obtain a target investment strategy to be recommended to users. This approach requires a large labor cost and cannot provide a personalized recommendation service for each user.
In order to solve the above problem, referring to fig. 1, fig. 1 is a structural diagram of an information recommendation system according to an embodiment of the present invention. The information recommendation system comprises a client 101 and a recommendation server 102, wherein the recommendation server 102 is in communication connection with a transaction server 103. Fig. 1 shows a connection relationship among the client 101, the recommendation server 102, and the transaction server 103, which represents a logical relationship when implementing the information recommendation method provided by the embodiment of the present invention. In practical implementation, the recommendation server 102 and the transaction server 103 may be different servers in a server cluster, and in this case, the recommendation server 102 and the transaction server 103 are communicatively connected through an external network. Alternatively, the recommendation server 102 and the transaction server 103 may be disposed in the same physical device, and in this case, the recommendation server 102 and the transaction server 103 may be communicatively connected through an internal communication protocol.
The client can be a mobile phone, a computer and other terminals. A program for acquiring the investment strategy is operated in the client. For example, when the client is a mobile phone, a program for acquiring the investment policy is installed in the mobile phone in the form of APP (Application). When the client is a computer, the program for acquiring the investment strategy is operated in a Web browser of the computer in the form of a Web page.
When the target user needs to acquire the investment strategy, the target user can input a strategy acquisition instruction to the client. For example, an "acquire investment strategy" operation button may be provided in the display interface of the client, and the user may click the "acquire investment strategy" operation button to input a strategy acquisition instruction to the client. When receiving the policy acquisition instruction, the client may send a corresponding information recommendation request to the recommendation server. Correspondingly, the recommendation server can interact with the transaction server based on the method provided by the embodiment of the invention, determine the target investment strategy recommended to the target user, and send recommendation information containing the target investment strategy to the client. Further, the client may display the recommendation information to the user in a display interface for the user to browse.
The information recommendation system provided by the embodiment of the invention forms a program for acquiring the investment strategy in the form of codes and provides the program for acquiring the investment strategy for the user. The client can display the target investment strategy to the user only by inputting a strategy acquisition instruction in the client running the program for acquiring the investment strategy, and the target investment strategy is the investment strategy according with the real requirement of the target user, so that the personalized recommendation service can be provided for the target user, and the information recommendation efficiency is improved.
Referring to fig. 2, fig. 2 is a flowchart of an information recommendation method provided in an embodiment of the present invention, where the method is applied to an information recommendation system, and the information recommendation system includes: a client and a recommendation server; the recommendation server is connected with the transaction server in a communication mode, and the method can comprise the following steps:
s201: and the client sends an information recommendation request containing the target user identification and the configuration information of the information to be recommended to the recommendation server.
S202: a recommendation server receives an information recommendation request; and under the condition that the information to be recommended is determined to be the investment strategy based on the configuration information, obtaining a plurality of investment strategies to be recommended based on the configuration information.
S203: and the recommendation server sends a corresponding user characteristic acquisition request to the transaction server based on the target user identification.
S204: the trading server obtains user characteristics of the target user based on historical investment data of the target user.
S205: the trading server returns the user characteristics of the target user to the recommendation server.
S206: and the recommending server sends a corresponding product characteristic obtaining request to the trading server aiming at each investment strategy to be recommended.
S207: and the transaction server acquires the product characteristics of the investment product corresponding to the investment strategy to be recommended.
S208: and the trading server returns the product characteristics corresponding to the investment strategy to be recommended to the recommending server.
S209: the recommendation server respectively calculates the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user, and the similarity is used as the similarity between the investment strategy to be recommended and the target user; and obtaining the investment strategy to be recommended, of which the similarity with the target user meets the preset selection condition, and taking the investment strategy as a candidate investment strategy.
S210: and the recommendation server sends a corresponding investment evaluation value acquisition request to the transaction server according to each candidate investment strategy.
S211: the transaction server obtains historical transaction scene information in a first historical time period according to the investment evaluation value obtaining request, and calculates first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; and calculating the target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information.
The first historical time period is a time period earlier than the time when the information recommendation request is received; the second historical time period is a time period during which simulated transactions are conducted based on the candidate investment strategy.
S212: and the trading server returns the target investment evaluation value of the candidate investment strategy to the recommendation server.
S213: and the recommendation server determines a target investment strategy of which the corresponding target investment evaluation value meets the preset recommendation condition from the candidate investment strategies.
S214: and the recommendation server sends the target investment strategy as recommendation information to the client.
S215: and the client displays the recommendation information.
Based on the information recommendation method provided by the embodiment of the invention, based on the user characteristics of the target user, the candidate investment strategies with the similarity to the user characteristics of the user meeting the preset selection conditions are determined, and the target investment strategies recommended to the user are determined from the candidate investment strategies, wherein the target investment strategies are the investment strategies meeting the real requirements of the target user, so that personalized recommendation service can be provided for the target user, and the candidate investment strategies are determined from the investment strategies to be recommended, so that the data volume of subsequent processing can be reduced, and the information recommendation efficiency can be improved.
For step S201 and step S202, the information to be recommended may be: video, music, merchandise, and investment strategies, etc. The target user can be any user needing information recommendation at present, and the target user identification can be the name, the mobile phone number, the account number of the login client and the like of the target user. One investment strategy includes: screening criteria for selecting an investment product and a trading mode for making an investment. The investment products may include: stocks, funds, treasures, futures, options, convertible debts, etc. The transaction mode may include: the number, time, and fund amount of investment products that are bought in line with the screening, and the number, time, and fund amount of investment products that are sold out.
When the target user needs to acquire the recommendation information, the target user can input an information acquisition instruction to the client. The client side can send an information recommendation request containing the target user identification and the configuration information of the information to be recommended to the recommendation server when receiving the information acquisition instruction.
In one implementation, the configuration information includes: policy identification of investment policies. Accordingly, step S202 may include the steps of: the recommendation server determines the investment strategy to which the strategy identification belongs from the investment strategies recorded locally to obtain a plurality of investment strategies to be recommended; wherein, each investment strategy to be recommended comprises a screening condition and a trading mode.
And when receiving the information acquisition instruction, the client can display the preset strategy identification of each investment strategy. The target user can select the investment strategy which is desired to be known from the preset investment strategies, and inputs a selection instruction carrying the strategy identification of the selected investment strategy to the client. Correspondingly, when the client receives a selection instruction for selecting the investment strategy, the client sends an information recommendation request carrying the strategy identification of the investment strategy selected by the user to the recommendation server. Correspondingly, the recommendation server may determine, from the plurality of investment strategies recorded locally, an investment strategy to which the strategy identifier carried in the information recommendation request belongs, as the investment strategy to be recommended. The investment strategy recorded in the recommendation server may be set by professionals (e.g., investment analysis professionals, investment managers, security company staff) who know the knowledge of the financial investment.
In another implementation manner, the configuration information of the information to be recommended includes: configuration parameter values for determining policy configuration items of the investment policies; configuring parameter values as input by a target user through a configuration page; a plurality of policy configuration items are displayed in the configuration page.
Accordingly, step S202 may include the steps of: the recommendation server generates a plurality of investment strategies to be recommended based on the configuration parameter values of the strategy configuration items input by the target user; wherein, each investment strategy to be recommended comprises product screening conditions and transaction modes.
The strategy configuration item is used for generating investment strategy, and the strategy configuration item can comprise: a policy configuration item (which may be referred to as a first policy configuration item) for determining a screening condition of the investment product, and a policy configuration item (which may be referred to as a second policy configuration item) for determining a transaction mode for making an investment. For example, the first policy configuration item may include: the corresponding opening price, market rate, operating profit, K-line graph type, etc. of the investment product, the second strategy configuration item may include: the number of purchases of the investment product, the time, the amount of funds, the number sold, the time, the amount of funds, etc.
When the client receives the policy acquisition instruction, the client can display a configuration page containing a plurality of policy configuration items. The target user may input configuration parameter values of each policy configuration item in the configuration page, for example, the configuration parameter values are used to input that the opening price corresponding to the investment product is greater than 1.01 yuan, the market net rate is greater than 2 times, the business profit is greater than 1 million, the K-line graph type is three-crow, the number of bought of the investment product is 3, the time is each transaction day, the number of fund is 50 ten thousand, the number of sold is 1, the time is each 3 transaction days, the number of fund is 10 ten thousand, and the like.
Further, the client may send configuration information including configuration parameter values for each policy configuration item to the recommendation server. Correspondingly, the recommendation server can determine the screening conditions and the transaction modes including the configuration parameter values of the strategy configuration items based on the configuration parameter values of the strategy configuration items input by the user, and then obtain the corresponding investment strategies to be used as the investment strategies to be recommended.
Based on the processing, the client can flexibly configure the investment strategy according to the instruction of the user to obtain the investment strategy meeting the user requirement, so that the individualized requirement of the investment strategy configured by the user can be met, and the user experience is improved.
For step S203 and step S204, the recommendation server may send a user obtaining request containing the target user identifier to the transaction server to obtain the user characteristics of the target user. When the transaction server receives the user characteristic acquisition request, the transaction server can acquire the user characteristics of the target user based on the historical investment data of the target user
The historical investment data of the target user may include product characteristics of the investment product historically invested by the target user. The user characteristics of the target user include: the target user prefers the risk level, product type, income condition, etc. when investing. The trading server may determine a user characteristic of the target user based on product characteristics of investment products historically invested by the target user. For example, if the investment product historically invested by the target user is an investment product with a lower risk level, the trading server may determine that the user characteristics of the target user include: the risk type is robust, and represents that the target user has low bearing capacity to the investment risk. Or, the investment product of the historical investment of the target user is a science and technology type investment product, and the trading server may determine that the user characteristics of the target user include: the product type is a science and technology class and represents that the target user prefers investment products of the science and technology class.
For step S206 and step S207, for each investment strategy to be recommended, the recommending server may send a product characteristic obtaining request for the investment strategy to be recommended to the trading server to obtain a product characteristic corresponding to the investment strategy to be recommended. When receiving the product feature acquisition request, the trading server may determine investment products (i.e., a first investment product and a second investment product in a subsequent embodiment) corresponding to the screening condition included in the investment strategy to be recommended, and acquire a feature tag of the investment product as a product feature corresponding to the investment strategy to be recommended. A signature of an investment product may include: the name, code, belonging plate, belonging industry, belonging company, investment risk level, accumulated yield, annual fluctuation rate, the number of position-holding funds of the investment product held by the target user, position-holding time and the like of the investment product.
In one embodiment, on the basis of fig. 2, referring to fig. 3, step S209 may include the steps of:
s2091: and the recommendation server performs mapping processing on the product characteristics corresponding to each investment strategy to be recommended according to the investment strategy to be recommended to obtain a first characteristic vector corresponding to the investment strategy to be recommended.
S2092: and the recommendation server performs mapping processing on the user characteristics of the target user to obtain a second characteristic vector corresponding to the target user.
S2093: and the recommendation server calculates the similarity between the first characteristic vector and the second characteristic vector to obtain the similarity between the product characteristic corresponding to the investment strategy to be recommended and the user characteristic of the target user, and the similarity is used as the similarity between the investment strategy to be recommended and the target user.
S2094: and the recommendation server determines the investment strategies to be recommended with the preset number from the investment strategies to be recommended as candidate investment strategies according to the sequence of similarity from the target user to the target user from large to small. Or determining the investment strategies to be recommended with the similarity greater than a preset threshold value with the target user from the investment strategies to be recommended as candidate investment strategies.
For each investment strategy to be recommended, the recommendation server may perform mapping processing on product features corresponding to the investment strategy to be recommended according to a preset coding mode to obtain a first feature vector corresponding to the investment strategy to be recommended. And mapping the user characteristics of the target user according to a preset coding mode to obtain a second characteristic vector corresponding to the target user. The preset encoding mode may be One-hot encoding, or embed encoding, etc.
The recommendation server can calculate the similarity between the first feature vector and the second feature vector based on a preset similarity algorithm to obtain the similarity between the investment strategy to be recommended and the target user. The preset similarity algorithm may be a cosine similarity algorithm, or may be an euclidean distance algorithm, or the like.
In one implementation manner, the recommendation server may determine, as candidate investment strategies, a preset number of investment strategies to be recommended from among the investment strategies to be recommended in an order from a greater similarity to a target user to a lesser similarity.
In another implementation manner, the recommendation server determines the investment strategies to be recommended, of which the similarity with the target user is greater than a preset threshold, from the investment strategies to be recommended, and uses the determined investment strategies to be recommended as candidate investment strategies.
For step S210 and step S211, for each candidate investment strategy, the recommendation server may send a corresponding investment evaluation value acquisition request to the trading server. Correspondingly, the trading server receives the investment evaluation value acquisition request, can acquire the first trading information and the second trading information corresponding to the candidate investment strategy so as to determine the target investment evaluation value of the candidate investment strategy, and returns the target investment evaluation value to the recommendation server.
The first historical time period is a time period earlier than the time when the information recommendation request is received; the second historical time period is a time period during which simulated transactions are conducted based on the candidate investment strategy. That is, the first historical period is earlier than the second historical period, and the duration of the first historical period and the duration of the second historical period may be set based on demand. For each candidate investment strategy, when the target user configures the candidate investment strategy at the client, the first historical time period may be set to be a time period before the time of configuring the candidate investment strategy, and the second historical time period may be set to be a time period after the time of configuring the candidate investment strategy.
For example, if the user configures the candidate investment strategy on day 2/1 of 2022, the first historical period may be set from day 1/1 of 2021 to day 31/12 of 2021, and the second historical period may be set from day 2/2 of 2022 to day 1/4 of 2022. On day 1/2/2022, the trading server may calculate first trading information meeting the target valuation index when making an investment in the investment market according to the candidate investment strategy over a first historical time period. The trading server can run the candidate investment strategy in real time from 2/2022 (namely, the starting time of the second historical time period), namely, based on the second product information of the second investment product, the trading server can conduct simulated trading according to the candidate investment strategy in real time until 1/4/2022 (namely, the ending time of the second historical time period), and second trading information which meets the target evaluation index when investment is conducted in the investment market according to the candidate investment strategy in the second historical time period can be obtained.
In one embodiment, referring to fig. 4, fig. 4 is a specific flowchart of acquiring transaction information according to an embodiment of the present invention, and the method may include the following steps:
s401: the trading server obtains product information of each investment product in a historical trading scene in a first historical time period as first product information.
S402: and the trading server determines each investment product matched with the screening condition in the candidate investment strategy from the investment products in the historical trading scene in the first historical time period as a first investment product.
S403: and the transaction server calculates transaction information which accords with the target evaluation index when investment is carried out according to the candidate investment strategy in a first historical time period as first transaction information based on the transaction mode in the candidate investment strategy and the first product information of the first investment product.
S404: and the trading server acquires the product information of each investment product in the historical trading scene in the second historical time period as second product information.
S405: and the trading server determines each investment product matched with the screening condition in the candidate investment strategy from the investment products in the historical trading scene in the second historical time period as a second investment product.
S406: and the transaction server carries out simulated transaction based on the transaction mode in the candidate investment strategy and second product information of the second investment product in a second historical time period to obtain transaction information meeting the target evaluation index as second transaction information.
In one implementation, a trading server may obtain product information (i.e., first product information) for each investment product within an investment market (i.e., historical trading scenario) for a first historical period of time. The product information for an investment product includes: market indicators representing price information for the investment product, such as the opening price, closing price, net market value, etc. for the investment product on each trading day of a time period. Financial index factors representing financial information of the company to which the investment product belongs, such as operating profits and financial statements of the company to which the investment product belongs each day in a time period. And form index factors of K line graph types representing the investment products, such as two crows, three crows, a rising triangle, a falling triangle, a head and shoulder top, a head and shoulder bottom and the like. A measure index factor representing a statistical characteristic of price information of the investment product, such as a day K line graph, a 5 day K line graph, a minute K line graph, a net worth curve for a time period, and the like, for the investment product. The public opinion index factor representing the public opinion information of the company to which the investment product belongs, for example, the number of news related to the company to which the investment product belongs, the browsing amount, and the user's evaluation of the company to which the investment product belongs. The characteristic data index factor represents the business information of the company to which the investment product belongs, such as the sales amount of the operation product of the company to which the investment product belongs, the frequency of the appeal of the company to which the investment product belongs, the number of social insurance paid by the company to which the investment product belongs, and the like.
Then, for each candidate investment strategy, the trading server may determine a first investment product from the investment products in the investment market over the first historical period of time that matches the screening criteria contained in the candidate investment strategy. For example, the candidate investment strategy comprises the following screening conditions: the opening price is greater than 1.01 yuan, and the trading server can determine the investment product with the opening price greater than 1.01 yuan as the first investment product. The trading server may calculate first trading information meeting the target valuation indicator when investing in accordance with the candidate investment strategy over a first historical time period based on the candidate investment strategy and first product information for the first investment product.
The transaction server determines the second investment product in a similar manner to the first investment product, reference is made to the description relating to the previous embodiments. Furthermore, the trading server may perform a simulated trade from a start time of the second historical time period according to the trading method in the candidate investment strategy and the second product information of the second investment product until an end time of the second historical time period, and may obtain second trading information meeting the target evaluation index when investment is performed in the investment market according to the candidate investment strategy in the second historical time period.
The transaction information over a time period includes at least one of: an initial amount of funds allowed to invest in the time period, an amount of funds remaining at the end of the last trade in the time period, a total equity per trading day in the time period, a daily rate of return, a number of first investment products held, an identification (e.g., a code) of each first investment product held, an amount of funds held, the total equity per trading day being: the sum of the remaining funds for the trading day and the total position funds for each of the first investment products held.
The transaction information that matches the target evaluation index is the transaction information used to calculate the index value of the target evaluation index. In acquiring the transaction information, the transaction server may determine transaction information (i.e., first transaction information) for calculating an index value of a target evaluation index based on the candidate investment strategy in a first history time period, and determine transaction information (i.e., second transaction information) for calculating an index value of a target evaluation index based on the candidate investment strategy in a second history time period.
For example, the target evaluation index is: and the accumulated yield rate is determined based on the beginning total equity and the end total equity of a time period, the beginning total equity of the time period is the initial fund number allowed to be invested at the starting moment of the time period, the end total equity of the time period is the sum of the residual fund number at the ending moment of the time period and the position fund number of the held investment product. Accordingly, the first trading information includes the initial and end equity for the first historical time period, and the trading server may calculate the initial and end equity for investing in the investment market according to the candidate investment strategy for the first historical time period.
Based on the processing, the target evaluation value of the candidate investment strategy is determined by combining the transaction information in the first historical time period and the transaction information in the second historical time period, so that the diversity of data can be improved, the calculated target investment evaluation value is more consistent with the actual situation, and the accuracy of the determined target investment evaluation value is improved.
In an embodiment of the present invention, referring to fig. 5, fig. 5 is a specific flowchart of determining an investment valuation value in an embodiment of the present invention, and the method may include the following steps:
s501: the transaction server calculates an index value of each target evaluation index as a first index value based on first transaction information conforming to the target evaluation index.
S502: the transaction server calculates a weighted sum of first index values of the target evaluation indexes as a first investment evaluation value.
S503: the transaction server calculates an index value of the target evaluation index as a second index value based on the second transaction information meeting the target evaluation index.
S504: the trading server calculates a weighted sum of the second index values of the target evaluation indexes as a second investment evaluation value.
S505: and the transaction server calculates the weighted sum of the first investment evaluation value and the second investment evaluation value to obtain a target investment evaluation value of the candidate investment strategy.
In one embodiment of the invention, the target evaluation index comprises at least one of: cumulative profitability, annual profitability, cumulative haphazard ratio, annual haphazard ratio, maximum withdrawal rate of gross equity, maximum number of days required for maximum withdrawal recovery of gross equity, annual volatility, cumulative excess profitability, annual excess profitability, maximum withdrawal rate of excess profitability, maximum number of days required for maximum withdrawal recovery of excess profitability, information ratio, daily unilateral handoff rate, annual unilateral handoff rate, daily bilateral handoff rate, and annual bilateral handoff rate.
For each candidate investment strategy, when the target evaluation index is the accumulated profitability, the trading server may calculate, based on first trading information corresponding to the accumulated profitability, the accumulated profitability invested in the first historical time period according to the candidate investment strategy as a first evaluation index value according to the following formula.
Figure BDA0003643564850000181
R represents the accumulated yield of investment according to the candidate investment strategy in a first historical time period; a represents the end total equity corresponding to the first historical time period, the end total equity corresponding to the first historical time period is the sum of the number of residual funds at the ending moment of the first historical time period and the number of position fund of the held investment product, and the end total equity corresponding to the first historical time period is the number of the residual funds at the ending moment of the first historical time period as follows: the amount of funds remaining at the end of the last transaction in the first historical period of time; and B represents the initial total equity corresponding to the first historical time period, and the initial total equity corresponding to the first historical time period is the initial fund number allowed to be invested at the starting moment of the first historical time period.
When the target evaluation index is an annual profitability, the trading server may calculate, based on first trading information corresponding to the annual profitability, the annual profitability of the investment performed according to the candidate investment strategy in the first historical time period as a first evaluation index value according to the following formula.
Figure BDA0003643564850000182
P represents the annual rate of return of investment according to the candidate investment strategy in the first historical time period; r represents the accumulated yield of investment according to the candidate investment strategy in a first historical time period; t represents the ratio of the number of trading days in a year to the number of trading days in the first historical time period.
When the target evaluation index is the accumulated sharp ratio, the trading server may calculate, as the first evaluation index value, a sharp ratio of investment based on the candidate investment strategy in the first historical time period according to the following formula based on the first trading information corresponding to the accumulated sharp ratio. The cumulative sharp ratio can measure the performance of the investment strategy relative to the risk-free combination, and is a measure of the risk premium obtained by the investment strategy, and the risk-free combination can be a decade national debt.
Figure BDA0003643564850000183
S represents the sharp rate of investment based on the candidate investment strategy in a first historical time period; r represents the accumulated yield of investment according to the candidate investment strategy in a first historical time period; f represents the accumulated yield of the risk-free combination in the first historical time period; μ represents the variance of the daily rate of return for each trading day invested in the first historical time period based on the candidate investment strategy.
When the target evaluation index is the maximum withdrawal rate of the total equity, the transaction server may calculate, based on the first transaction information corresponding to the maximum withdrawal rate of the total equity, the maximum withdrawal rate of the total equity invested based on the candidate investment policy in the first historical time period according to the following formula, and use the maximum withdrawal rate as the first evaluation index value. The maximum withdrawal rate of the gross equity is an important index for assessing the extreme risk management capabilities of the investment strategy.
Figure BDA0003643564850000191
D represents the maximum withdrawal rate of the total rights and interests of investment based on the candidate investment strategy in the first historical time period; c 1 Representing a maximum value of the total equity for each trading day invested in the candidate investment strategy over a first historical period of time; c 2 Representing a minimum value of the total equity for each trading day invested in the candidate investment strategy over the first historical period of time.
When the target evaluation index is an annual fluctuation rate, the trading server may calculate, as the first evaluation index value, an annual fluctuation rate of investment performed based on the candidate investment strategy in the first historical time period according to the following formula based on the first trading information corresponding to the annual fluctuation rate. The annual fluctuation rate is a common risk measurement index, and the higher the annual fluctuation rate is, the higher the risk of the investment strategy is.
Figure BDA0003643564850000192
σ represents the annual volatility of investing in the first historical period based on the candidate investment strategy, n represents the number of trading days in the first historical period, and r (i) represents the second investment in the first historical period based on the candidate investment strategyThe daily rate of return for the i trading days,
Figure BDA0003643564850000193
representing an average of daily rates of return for each trading day invested in the first historical period based on the candidate investment strategy.
When the target evaluation index is the accumulated excess earning rate, the trading server may calculate, based on first trading information corresponding to the accumulated excess earning rate, the accumulated excess earning rate of investment performed based on the candidate investment strategy in the first historical time period according to the following formula, as the first evaluation index value. The calculation formula for the accumulated excess profitability is a residual term in the expression of CAPM (Capital Asset Pricing Model). The cumulative excess profitability can represent the portion of the income corresponding to the investment strategy that is not related to the overall income of the market, and can be used for measuring the capacity of the investment strategy for selecting investment products. The accumulated excess yield is a positive value, which indicates that the yield of the investment product selected by the investment strategy is higher than the market benchmark combination; the accumulated excess yield is negative, which indicates that the yield of the investment product selected by the investment strategy is lower than the market benchmark combination, and the market benchmark combination is the Shanghai 300 index.
α＝E[r(i)-[f+β(r(i)-f)]] (6)
Alpha represents the accumulated excess yield of investment based on the candidate investment strategy in the first historical time period; e represents a mathematical expectation; r (i) represents the daily rate of return for the ith trading day of investing in the first historical period based on the candidate investment strategy; f represents the daily rate of return of risk-free combinations in the first historical time period; beta represents a preset coefficient in the CAPM model.
When the target evaluation index is the information ratio, the trading server may calculate, as the first evaluation index value, an information ratio of investing in the first history time period based on the candidate investment strategy according to the following formula based on the first trading information corresponding to the information ratio. The information rate is used to measure the profitability of the investment strategy relative to the market benchmark portfolio. The information ratios are generally used to evaluate purely multi-tiered, proactive investment strategies (e.g., alpha investment strategies and benchmarking investment strategies), and the information ratios are not suitable for evaluating multi-space combined hedge investment strategies.
Figure BDA0003643564850000201
I represents the information rate of investment based on the candidate investment strategy in a first historical time period; r represents the accumulated yield of investment according to the candidate investment strategy in a first historical time period; f represents the accumulated yield of risk-free combinations in the first historical time period; ε represents the variance of the daily profitability of the market benchmark portfolio over the first historical time period.
When the target evaluation index is the daily unilateral handover rate, the trading server can calculate the ratio of the number of funds for buying the first investment product on the trading day to the total rights and interests of the trading day for each trading day in the first historical time period to obtain the daily unilateral handover rate of the trading day, and calculate the average value of the daily unilateral handover rates of the trading days in the first historical time period as the first index value.
In addition, the aged sharp ratio may be calculated based on the accumulated sharp ratio. The maximum number of days required for withdrawal recovery of the total equity represents the number of days between the trading day with the minimum value of the total equity and the trading day with the maximum value of the total equity when investment is conducted according to the candidate investment strategy in the first historical time period. The annual excess rate can be calculated based on the cumulative excess rate. The maximum withdrawal rate of the excess rate of return and the number of days required for the maximum withdrawal recovery of the excess rate of return can be calculated and obtained based on the excess rate of return of each trading day in the first historical time period. The annual unilateral handoff rate can be calculated based on the daily unilateral handoff rate. For each trading day in the first historical time period, the daily bilateral hand-off rate of the trading day is the ratio of the total fund number of the first investment product bought and sold in the trading day to the total right and interest of the trading day, and the trading server can calculate the average daily bilateral hand-off rate of each trading day in the first historical time period as the first index value. The annual bilateral hand-changing rate can be calculated based on the daily bilateral hand-changing rate.
Further, the trading server may calculate a weighted sum of the first index values of the target evaluation indexes to obtain the first investment evaluation value. For example, the transaction server may record the correspondence between the target evaluation index and the weight in the form of series values (data corresponding to values and indexes). Referring to table 1, table 1 is a table of correspondence between target evaluation indexes and weights provided in the embodiment of the present invention. The sum of the weights of the target evaluation indexes is 1.
TABLE 1
Evaluation index Weight of
zb-a zb-a1
zb-b zb-b1
zb-c zb-c1
zb-d zb-d1
zb-e zb-e1
zb-f zb-f1
zb-g zb-g1
zb-a to zb-g represent different target evaluation indexes, and zb-a1 to zb-g1 represent weights of the corresponding target evaluation indexes. The trading server may calculate index values of the evaluation indexes zb-a to zb-g based on the first trading information corresponding to the first historical time period, and then the trading server may calculate a weighted sum of the index values of the evaluation indexes zb-a to zb-g according to the weights zb-a1 to zb-g1 to obtain a first investment evaluation value.
The trading server calculates the second investment valuation in a similar manner to the first investment valuation, as described in relation to the previous embodiments.
Based on the processing, a plurality of different evaluation indexes can be integrated to obtain the investment evaluation value of the candidate investment strategy, and the investment evaluation value can embody the characteristics of the investment strategy from a plurality of dimensions.
For each candidate investment strategy, the trading server may calculate a weighted sum of the first investment rating and the second investment rating of the candidate investment strategy to obtain a target investment rating of the candidate investment strategy. The first investment valuation has a weight of a, the second investment valuation has a weight of b, and the sum of a and b is 1. For example, the first investment valuation has a weight of 0.1, and the second investment valuation has a weight of 0.9. The trading server can also obtain the final investment evaluation value of the candidate investment strategy within the range of 0 to 100 of the target investment evaluation value normalization value.
Exemplary target evaluation indicators include: cumulative profitability, cumulative sharp rate and maximum rate of total equity. The weight of the cumulative rate of return is 0.8, the weight of the cumulative sharp ratio is 0.1, and the weight of the maximum withdrawal rate of the total equity is 0.1. The first investment evaluation value is: a weighted sum of the cumulative rate of return, the cumulative sharp rate, and the maximum rate of withdrawal of the aggregate equity over the first historical period of time. The second investment evaluation value is: a weighted sum of the cumulative rate of return, the cumulative sharp rate, and the maximum rate of withdrawal of the aggregate equity for the second historical period of time. The target evaluation indexes are as follows: 0.1 × first investment evaluation value +0.9 × second investment evaluation value. And obtaining the final investment evaluation value of the candidate investment strategy by normalizing the target investment evaluation value within the range of 0-100.
With respect to step S213 and step S214, the recommendation server may determine, from the candidate investment strategies, the investment strategies of the first preset number in order of the target investment evaluation value from large to small, as the target investment strategy, for example, determine, from the candidate investment strategies, the investment strategy with the largest corresponding target investment evaluation value, as the target investment strategy. Further, the recommendation server may send recommendation information including the target investment strategy and the corresponding target investment evaluation value to the client.
With respect to step S215, the client may receive the recommendation information sent by the recommendation server and display the recommendation information to the target user, that is, display the target investment strategy and the corresponding target investment evaluation value for the user to browse. Subsequently, the user can invest according to the target investment strategy.
In addition, the recommendation server can also sort the candidate investment strategies according to the sequence of the target investment evaluation values from large to small, take the sorting result as recommendation information, and send the recommendation information to the client, that is, send the sorting result to the client. The client may display the received recommendation information, that is, display each candidate investment strategy and the corresponding target investment evaluation value in the order of the target investment evaluation values from large to small. The user may then determine the target investment strategy based on the displayed target investment evaluation values for each candidate investment strategy.
In one embodiment, for each investment strategy to be recommended, the recommendation server may obtain a target investment evaluation value of the investment strategy to be recommended and a similarity between the investment strategy to be recommended and a target user. Then, the recommendation server may calculate a weighted sum of the target investment evaluation value of the investment strategy to be recommended and the corresponding similarity as a final investment evaluation value of the investment strategy to be recommended. Then, the recommendation server may determine, from the investment strategies to be recommended, the investment strategies of the previous preset number as the target investment strategy in the order from the largest to the smallest of the final investment evaluation values.
In an embodiment, for each to-be-recommended investment strategy, the recommendation server may obtain a target investment evaluation value of the to-be-recommended investment strategy, and determine, from the to-be-recommended investment strategies, an investment strategy in which the investment evaluation value satisfies a preset recommendation condition as a candidate investment strategy. Then, for each candidate investment strategy, the similarity between the candidate investment strategy and the target user is obtained. Then, the recommendation server may determine, from the investment strategies to be recommended, an investment strategy whose corresponding similarity satisfies a preset selection condition as a target investment strategy.
In one embodiment, the trading server may further obtain a target evaluation index, and further calculate a target investment evaluation value of the candidate investment strategy according to the target evaluation index, the first trading information and the second trading information.
In one implementation, the configuration information further includes: a plurality of target evaluation indexes determined by a target user through a configuration page; the investment evaluation value acquisition request comprises a plurality of target evaluation indexes, so that the trading server determines the target investment evaluation value of the candidate investment strategy based on the target evaluation indexes, the first trading information and the second trading information.
The target user can input an index configuration instruction for acquiring the evaluation index to the client. When receiving the index configuration instruction, the client may display a configuration page including a plurality of preset evaluation indexes. The user can select a target evaluation index for evaluating the investment strategy from the plurality of evaluation indexes so as to input an index selection instruction to the client. For example, the configuration page displays a plurality of evaluation indexes such as an accumulated profitability, an aged profitability, an accumulated sharp rate, an information rate, and aged fluctuations. The target user can select the target evaluation index as follows: annual profitability, information rates, and annual fluctuations. Then, the client may send an information recommendation request containing the target evaluation index to the recommendation server. Subsequently, the recommendation server may send an investment evaluation value acquisition request including the target evaluation index to the trading server, and the trading server may determine the target investment evaluation value of each candidate investment strategy based on the target evaluation index, the first trading information, and the second trading information.
Based on the processing, the client can flexibly configure the evaluation index according to the instruction of the user to obtain the target evaluation index meeting the user requirement, so that the personalized requirement of the user for configuring the evaluation index can be met, and the user experience is improved.
In another implementation manner, the investment evaluation value acquisition request includes a target user identifier, so that the transaction server determines an evaluation index matched with the user characteristic of the target user from preset evaluation indexes to serve as a target evaluation index; and determining the target investment evaluation value of the candidate investment strategy based on the target evaluation index, the first transaction information and the second transaction information.
Illustratively, the user characteristics of the target user include: when the risk type is robust, the trading server may determine, as a target evaluation index, an evaluation index (e.g., annual volatility, maximum withdrawal rate of total equity, sharp rate, etc.) that can represent a risk level of the investment strategy. Alternatively, the user characteristics of the target user include: when the profit scenario is higher than 1%, the trading server may determine, as the target evaluation index, an evaluation index (for example, cumulative profit rate, annual profit rate, excess profit rate, or the like) that can represent the profit scenario corresponding to the investment strategy.
Based on the processing, the target evaluation index is an evaluation index matched with the user characteristics of the target user, the target evaluation index meets the requirements of the target user, the evaluation index can be configured aiming at the target user in a personalized mode, and the user experience is improved.
Referring to fig. 6, fig. 6 is a schematic diagram of a configuration page of a client according to an embodiment of the present invention. The user can configure investment policies and evaluation indexes in the configuration page. The strategy configuration items for configuring the investment strategy include: stock selection settings (for setting the stock's screening conditions), trading models (i.e., the trading style in which the investment is made), and risk control (i.e., the risk type of the stock).
Selecting a stock pool to be a full market indicates that the user may select all stocks in the investment market. A plate is a stock that all represent any plate that a user can select (e.g., military and semiconductor, etc.). Industry is stock that all represent any industry that a user may select (e.g., medical and new energy, etc.). Filtering the ST means filtering the ST type stocks, wherein the ST type stocks refer to stocks of which the company has financial abnormal conditions or other abnormal conditions. The user can input the required stock selection index or the evaluation index in the search factor input box so as to quickly select the required stock selection index or the evaluation index.
The stock selection indexes comprise: market conditions (i.e., market condition indicator factor in the previous embodiment), technical indicators (i.e., volume price indicator factor in the previous embodiment), financial indicators and financial data (i.e., financial indicator factor in the previous embodiment), and morphological indicators (i.e., morphological indicator factor in the previous embodiment).
The market conditions include: the opening price, closing price, highest price, lowest price, volume of trades, hand-off rate and days on market of stock. For example, the user may select the opening price as the stock selection index. The technical indexes comprise: stock mean, MACD (Moving Average conversion/Divergence), aloron Index, KDJ (random Index), ROC (Price Rate of Change), MFI (Money Flow Index), brink line, TRIX (Triple exponentiation smooth Moving Average), and the like. The financial indexes include: the valuation, profitability, index per share, repayment ability and the like of the company to which the stock belongs.
My indicator represents the number of stock selection indicators that the user has selected, e.g., in fig. 6 the user has selected 4 stock selection indicators, which are: opening price, market rate, operating profit and two crows. The range of the opening price is more than 1.01 yuan, the net market value is more than 2 times, and the operating profit is more than 1000 ten thousand. The delete button "x" is used to delete the selected stock selection index. The 'adding custom rule' is used for adding stock selection conditions set by the user.
The transaction model includes: the system comprises a regular rotation mode and a condition triggering mode, wherein the regular rotation mode represents a period for setting the adjustment of the position-holding fund number of the investment product by a user, maximum position-holding stock data and the like. The condition trigger mode means that the user sets the buying condition and selling condition of the stock. The transaction model that the user has selected in fig. 6 is a periodically recurring manner.
After setting the parameter values and evaluation indexes of the policy configuration items, the user may click a "save" button to input configuration information including the parameter values and target evaluation indexes of the policy configuration items to the client. Based on the processing, the user can configure the investment strategy and the evaluation index on the configuration page, so that the personalized configuration requirement of the user can be met, and the user experience is improved.
Referring to fig. 7, fig. 7 is a schematic diagram illustrating an information recommendation method according to an embodiment of the present invention.
Sources of quotations and factors include: shanghai and Shen stock exchange, Chinese financial futures exchange, and the trade of the big-link commodities. The quotation factor access device is used for acquiring the product information such as the quotation index factors, the financial index factors, the form index factors, the volume index factors, the public opinion index factors and the characteristic data index factors of all investment products in an investment market in a specified time period (such as a first historical time period and a second historical time period) from a quotation and factor source, and the quotation factor access device can be realized by a trading server when being specifically realized.
The quantitative strategy making device is used for receiving the investment strategy configured by the user. The quantitative policy making means provides the user with a uniform amount of virtual funds (for example, 100 ten thousand virtual funds) as the initial amount of funds allowed to make an investment. The quantitative strategy making device comprises a stock selection module, a transaction model and a wind control model. The user sets stock selection conditions (namely stock screening conditions) through a stock selection module of the quantitative strategy making device, and the stock selection conditions configured by the stock selection module comprise: market conditions (i.e., market condition indicator factor in the foregoing embodiment), technical indicators (i.e., volume price indicator factor in the foregoing embodiment), financial indicators and financial data (i.e., financial indicator factor in the foregoing embodiment), morphological indicators (i.e., morphological indicator factor in the foregoing embodiment), and the like.
The user sets a transaction mode for investment through a transaction model of the quantitative strategy making device, and the transaction mode comprises the following steps: the system comprises a regular rotation mode and a condition triggering mode, wherein the regular rotation mode represents a period for setting the adjustment of the position-holding fund number of the investment product by a user, maximum position-holding stock data and the like. The condition triggering mode means that the user sets buying conditions and selling conditions of stocks. The quantization strategy making device may be implemented by a recommendation server.
Further, when the product information of the stock satisfies the buying condition, the quantitative strategy operating device will buy the stock according to the investment strategy to conduct stock buying transaction. When the product information of the stock meets the selling condition, the quantitative strategy operating device sells the held stock according to the investment strategy so as to carry out stock selling transaction. The quantitative strategy operation device uniformly sets the starting operation time and the ending operation time of the investment strategy (namely, the starting time and the ending time of the historical time period are determined), different investment strategies are operated in a program mode according to the product information acquired by the market factor access device, and when the product information of the investment product meets the purchase condition, the quantitative strategy operation device purchases the investment product according to the investment strategy. When the product information of the investment product meets the selling condition, the quantitative strategy operating device sells the held investment product according to the investment strategy. And the quantization strategy operation device performs warehouse change operation according to the product information such as the investment strategy, the market value of the investment product and the market value of the market. The market value of a position taken is the product of the position taken price and the position taken quantity of the investment product. And, the quantitative strategy operation device can also record the position taking situation (namely transaction information) of the investment strategy operation. The taken position condition comprises the following steps: the amount of remaining funds, the total equity, the code, number of investment products held, the net real-time market value, and the average of the bargain prices for purchasing the investment products, etc.
In addition, the quantitative policy operation device is also provided with a transaction commission deduction standard, and when the warehouse change operation is performed, the corresponding transaction commission is deducted according to the transaction commission deduction standard when investment is performed, for example, the lowest transaction commission is 5 yuan, and the two-way transaction commission is ten-thousandth of the fund amount of the transaction, and the like. The quantization strategy operation device can be realized by a trading server when being specifically realized.
The quantitative strategy evaluation device is used for calculating a target investment evaluation value of the candidate investment strategy based on the transaction information invested according to the candidate investment strategy and determining recommendation information containing the target investment strategy recommended to the target user. The quantitative policy evaluation device may be implemented by a transaction server.
And the quantitative strategy evaluation output device is used for sending the recommendation information to the client so that the client displays the recommendation information to the target user. The quantization strategy evaluation output device can be realized by a recommendation server when being realized specifically.
Based on the information recommendation method provided by the embodiment of the invention, an investment strategy evaluation platform is provided for the user, and the investment strategy can be evaluated by combining the first historical time period and the second historical time period to obtain a target investment evaluation value according with the actual situation. The investment strategy is operated based on the real-time market index factors corresponding to the historical time period, and the fund for operating the investment strategy is virtual fund, so that the simulation can be conveniently performed before the investment strategy is actually operated. The user can flexibly configure the investment strategy and the evaluation index, the configuration mode is simple, the operation is convenient, the individual requirements of the user can be met, and the user experience is improved.
Corresponding to the embodiment of the method in fig. 1, referring to fig. 8, fig. 8 is a structural diagram of a recommendation server provided in an embodiment of the present invention, where the recommendation server includes:
an information recommendation request receiving module 801, configured to receive an information recommendation request sent by the client; the information recommendation request comprises a target user identifier and configuration information of information to be recommended; a to-be-recommended investment strategy obtaining module 802, configured to, when it is determined that the to-be-recommended information is an investment strategy based on the configuration information, obtain a plurality of to-be-recommended investment strategies based on the configuration information; a user characteristic obtaining module 803, configured to send a corresponding user characteristic obtaining request to the transaction server based on the target user identifier; so that the transaction server obtains the user characteristics of the target user based on the transaction data of the historical investment of the target user and returns the user characteristics to the recommendation server; a product characteristic obtaining module 804, configured to send, to the trading server, a corresponding product characteristic obtaining request for each investment strategy to be recommended; so that the trading server obtains the product characteristics of the investment product corresponding to the investment strategy to be recommended and returns the product characteristics to the recommending server; the similarity calculation module 805 is configured to calculate a similarity between a product characteristic corresponding to each investment strategy to be recommended and a user characteristic of the target user, as a similarity between the investment strategy to be recommended and the target user; a candidate investment strategy obtaining module 806, configured to obtain an investment strategy to be recommended, for which the similarity to the target user meets a preset selection condition, as a candidate investment strategy; an investment evaluation value acquisition module 807 for sending a corresponding investment evaluation value acquisition request to the trading server for each candidate investment strategy; the transaction server obtains historical transaction scene information in a first historical time period according to the investment evaluation value obtaining request, and calculates first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server; a target investment strategy obtaining module 808, configured to determine, from among the candidate investment strategies, a target investment strategy for which a corresponding target investment evaluation value satisfies a preset recommendation condition; and the recommendation information output module 809 is configured to send the target investment strategy as recommendation information to the client, so that the client displays the recommendation information.
Optionally, the configuration information includes: configuration parameter values for determining policy configuration items of the investment policy; the configuration parameter value is input by the target user through a configuration page; a plurality of policy configuration items are displayed in the configuration page;
the investment strategy to be recommended acquisition module 802 is specifically configured to generate a plurality of investment strategies to be recommended based on configuration parameter values of the strategy configuration items input by the target user; wherein, each investment strategy to be recommended comprises a screening condition and a trading mode.
Optionally, the configuration information further includes: the target user determines a plurality of target evaluation indexes through the configuration page; the investment evaluation value acquisition request comprises a plurality of target evaluation indexes, so that the trading server determines a target investment evaluation value of the candidate investment strategy based on the target evaluation indexes, the first trading information and the second trading information;
alternatively, the first and second liquid crystal display panels may be,
the investment evaluation value acquisition request comprises the target user identification, so that the transaction server determines an evaluation index matched with the user characteristic of the target user from preset evaluation indexes to serve as a target evaluation index; and determining the target investment evaluation value of the candidate investment strategy based on the target evaluation index, the first transaction information and the second transaction information.
Optionally, the configuration information includes: strategy identification of investment strategy;
the investment strategy to be recommended obtaining module 802 is specifically configured to determine an investment strategy to which the strategy identifier belongs from among the locally recorded investment strategies, and obtain a plurality of investment strategies to be recommended; wherein, each investment strategy to be recommended comprises a screening condition and a trading mode.
Optionally, the similarity calculation module 805 is specifically configured to, for each investment strategy to be recommended, perform mapping processing on product features corresponding to the investment strategy to be recommended to obtain a first feature vector corresponding to the investment strategy to be recommended; mapping the user characteristics of the target user to obtain a second characteristic vector corresponding to the target user; calculating the similarity between the first feature vector and the second feature vector to obtain the similarity between the product features corresponding to the investment strategy to be recommended and the user features of the target user, and taking the similarity as the similarity between the investment strategy to be recommended and the target user;
the candidate investment strategy obtaining module 806 is specifically configured to determine, from the investment strategies to be recommended, a preset number of investment strategies to be recommended as candidate investment strategies according to a descending order of similarity with the target user;
alternatively, the first and second electrodes may be,
and determining the investment strategies to be recommended with the similarity greater than a preset threshold value with the target user from the investment strategies to be recommended as candidate investment strategies.
Based on the recommendation server provided by the embodiment of the invention, based on the user characteristics of the target user, the candidate investment strategies with the similarity to the user characteristics of the user meeting the preset selection conditions are determined, and the target investment strategies recommended to the user are determined from the candidate investment strategies, wherein the target investment strategies are the investment strategies meeting the real requirements of the target user, so that personalized recommendation service can be provided for the target user, and the candidate investment strategies are determined from the investment strategies to be recommended, so that the data volume of subsequent processing can be reduced, and the information recommendation efficiency can be improved.
Corresponding to the embodiment of the method in fig. 1, referring to fig. 9, fig. 9 is a structural diagram of a transaction server according to an embodiment of the present invention, where the transaction server includes:
a user characteristic obtaining module 901, configured to, when receiving a user characteristic obtaining request sent by the recommendation server based on a target user identifier included in the information recommendation request, obtain a user characteristic of the target user based on historical investment data of the target user, and return the user characteristic to the recommendation server; the information recommendation request is sent to the recommendation server by a client, and comprises a target user identifier and configuration information of information to be recommended; a product characteristic obtaining module 902, configured to, for each investment strategy to be recommended, obtain, when receiving a product characteristic obtaining request sent by the recommendation server, a product characteristic of an investment product corresponding to the investment strategy to be recommended, and return the product characteristic to the recommendation server; the recommendation server respectively calculates the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user, and the similarity is used as the similarity between the investment strategy to be recommended and the target user; acquiring an investment strategy to be recommended, of which the similarity with the target user meets a preset selection condition, and taking the investment strategy as a candidate investment strategy; wherein, the investment strategy to be recommended is as follows: the recommendation server is obtained based on the configuration information under the condition that the information to be recommended is determined to be an investment strategy based on the configuration information; a transaction information obtaining module 903, configured to, for each candidate investment policy, obtain, for an investment evaluation value obtaining request sent by the recommendation server, historical transaction scenario information in a first historical time period according to the investment evaluation value obtaining request, and calculate, based on the historical transaction scenario information, first transaction information corresponding to the candidate investment policy; the first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; an investment evaluation value obtaining module 904, configured to calculate a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and return the target investment evaluation value to the recommendation server; so that the recommendation server determines a target investment strategy that the corresponding target investment evaluation value meets the preset recommendation condition from the candidate investment strategies; and sending the target investment strategy as recommendation information to the client so as to enable the client to display the recommendation information.
Optionally, the trading information obtaining module 903 is specifically configured to obtain product information of each investment product in a historical trading scenario within the first historical time period, as first product information; determining each investment product matched with the screening condition in the candidate investment strategy from the investment products in the historical trading scene in the first historical time period as a first investment product; calculating transaction information which accords with the target evaluation index when investment is carried out according to the candidate investment strategy in the first historical time period based on the transaction mode in the candidate investment strategy and the first product information of the first investment product, and taking the transaction information as first transaction information;
the trading information obtaining module 903 is specifically configured to obtain product information of each investment product in a historical trading scenario within the second historical time period, as second product information; determining each investment product matched with the screening condition in the candidate investment strategy from each investment product in the historical trading scene in the second historical time period as a second investment product; and in the second historical time period, performing simulated transaction based on the transaction mode in the candidate investment strategy and the second product information of the second investment product to obtain transaction information which meets the target evaluation index and is used as second transaction information.
Optionally, the investment evaluation value acquisition request carries a plurality of target evaluation indexes;
the investment evaluation value acquisition module 904 is specifically configured to calculate, for each target evaluation index, an index value of the target evaluation index as a first index value based on the first transaction information that meets the target evaluation index; calculating a weighted sum of first index values of the target evaluation indexes as a first investment evaluation value; calculating an index value of the target evaluation index as a second index value based on the second transaction information conforming to the target evaluation index; calculating the weighted sum of the second index values of the target evaluation indexes to serve as a second investment evaluation value; and calculating the weighted sum of the first investment evaluation value and the second investment evaluation value to obtain the target investment evaluation value of the candidate investment strategy.
Based on the transaction server provided by the embodiment of the invention, based on the user characteristics of the target user, the candidate investment strategies with the similarity to the user characteristics of the user meeting the preset selection conditions are determined, and the target investment strategies recommended to the user are determined from the candidate investment strategies, wherein the target investment strategies are the investment strategies meeting the real requirements of the target user, so that personalized recommendation service can be provided for the target user, and the candidate investment strategies are determined from the investment strategies to be recommended, so that the data volume of subsequent processing can be reduced, and the information recommendation efficiency can be improved.
In still another embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the information recommendation methods applied to the recommendation server described in the embodiments or the steps of any of the information recommendation methods applied to the transaction server described in the embodiments above.
In yet another embodiment of the present invention, there is also provided a computer program product containing instructions, which when run on a computer, causes the computer to execute the information recommendation method applied to the recommendation server in any of the above embodiments, or the steps of the information recommendation method applied to the transaction server in any of the above embodiments.
In the above embodiments, all or part of the implementation may be realized by software, hardware, firmware, or any combination thereof. When implemented in software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to be performed in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system, recommendation server, transaction server, computer-readable storage medium, and computer program product embodiments are described in a relatively simple manner as they are substantially similar to the method embodiments, as may be relevant with reference to the partial description of the method embodiments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (10)

1. An information recommendation method is characterized in that the method is applied to a recommendation server in a recommendation system, and the recommendation system further comprises a client; the recommendation server is in communication connection with the transaction server, and the method comprises the following steps:
receiving an information recommendation request sent by the client; the information recommendation request comprises a target user identifier and configuration information of information to be recommended;
under the condition that the information to be recommended is determined to be the investment strategy based on the configuration information, obtaining a plurality of investment strategies to be recommended based on the configuration information;
sending a corresponding user characteristic acquisition request to the transaction server based on the target user identification; so that the transaction server obtains the user characteristics of the target user based on the historical investment data of the target user and returns the user characteristics to the recommendation server;
sending a corresponding product characteristic acquisition request to the trading server aiming at each investment strategy to be recommended; so that the trading server obtains the product characteristics of the investment product corresponding to the investment strategy to be recommended and returns the product characteristics to the recommending server;
respectively calculating the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user, and taking the similarity as the similarity between the investment strategy to be recommended and the target user;
acquiring an investment strategy to be recommended, of which the similarity with the target user meets a preset selection condition, and taking the investment strategy as a candidate investment strategy;
sending a corresponding investment evaluation value acquisition request to the transaction server aiming at each candidate investment strategy; the transaction server obtains historical transaction scene information in a first historical time period according to the investment evaluation value obtaining request, and calculates first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server;
determining a target investment strategy of which the corresponding target investment evaluation value meets a preset recommendation condition from the candidate investment strategies;
and sending the target investment strategy as recommendation information to the client so as to enable the client to display the recommendation information.
2. The method of claim 1, wherein the configuration information comprises: configuration parameter values for determining policy configuration items of the investment policies; the configuration parameter value is input by the target user through a configuration page; a plurality of strategy configuration items are displayed in the configuration page;
the obtaining a plurality of investment strategies to be recommended based on the configuration information under the condition that the information to be recommended is determined to be the investment strategy based on the configuration information comprises the following steps:
generating a plurality of investment strategies to be recommended based on configuration parameter values of the strategy configuration items input by the target user; wherein, each investment strategy to be recommended comprises a screening condition and a trading mode.
3. The method of claim 2, wherein the configuration information further comprises: the target user determines a plurality of target evaluation indexes through the configuration page; the investment evaluation value acquisition request comprises a plurality of target evaluation indexes, so that the trading server determines a target investment evaluation value of the candidate investment strategy based on the target evaluation indexes, the first trading information and the second trading information;
alternatively, the first and second electrodes may be,
the investment evaluation value acquisition request comprises the target user identification, so that the transaction server determines an evaluation index matched with the user characteristic of the target user from preset evaluation indexes to serve as a target evaluation index; and determining the target investment evaluation value of the candidate investment strategy based on the target evaluation index, the first transaction information and the second transaction information.
4. The method of claim 1, wherein the configuration information comprises: strategy identification of investment strategy;
the obtaining a plurality of investment strategies to be recommended based on the configuration information under the condition that the information to be recommended is determined to be the investment strategy based on the configuration information comprises the following steps:
determining the investment strategies to which the strategy identifications belong from the investment strategies recorded locally to obtain a plurality of investment strategies to be recommended; wherein, each investment strategy to be recommended comprises a screening condition and a trading mode.
5. The method according to claim 1, wherein the calculating the similarity between the product feature corresponding to each investment strategy to be recommended and the user feature of the target user as the similarity between the investment strategy to be recommended and the target user comprises:
aiming at each investment strategy to be recommended, mapping the product characteristics corresponding to the investment strategy to be recommended to obtain a first characteristic vector corresponding to the investment strategy to be recommended;
mapping the user characteristics of the target user to obtain a second characteristic vector corresponding to the target user;
calculating the similarity between the first feature vector and the second feature vector to obtain the similarity between the product features corresponding to the investment strategy to be recommended and the user features of the target user, and taking the similarity as the similarity between the investment strategy to be recommended and the target user;
the step of obtaining the investment strategy to be recommended with the similarity to the target user meeting the preset selection condition as a candidate investment strategy comprises the following steps:
determining a preset number of investment strategies to be recommended from the investment strategies to be recommended as candidate investment strategies according to the sequence of similarity from the target user to the target user from large to small;
alternatively, the first and second electrodes may be,
and determining the investment strategies to be recommended with the similarity greater than a preset threshold value with the target user from the investment strategies to be recommended as candidate investment strategies.
6. The information recommendation method is applied to a trading server, and the trading server is in communication connection with a recommendation server in a recommendation system; the recommendation system further comprises a client; the method comprises the following steps:
when a user characteristic obtaining request sent by the recommendation server based on a target user identifier contained in an information recommendation request is received, obtaining the user characteristics of a target user based on historical investment data of the target user, and returning the user characteristics to the recommendation server; the information recommendation request is sent to the recommendation server by a client, and comprises a target user identifier and configuration information of information to be recommended;
for each investment strategy to be recommended, when a product characteristic acquisition request sent by the recommendation server is received, acquiring the product characteristics of the investment product corresponding to the investment strategy to be recommended, and returning the product characteristics to the recommendation server; the recommendation server respectively calculates the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user, and the similarity is used as the similarity between the investment strategy to be recommended and the target user; acquiring an investment strategy to be recommended, of which the similarity with the target user meets a preset selection condition, and taking the investment strategy as a candidate investment strategy; wherein, the investment strategy to be recommended is as follows: the recommending server obtains the information to be recommended based on the configuration information under the condition that the information to be recommended is determined to be an investment strategy based on the configuration information;
for each candidate investment strategy, when an investment evaluation value acquisition request sent by the recommendation server is received, acquiring historical transaction scene information in a first historical time period for the investment evaluation value acquisition request, and calculating first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and acquiring second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy;
calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server; so that the recommendation server determines a target investment strategy, of which the corresponding target investment evaluation value meets preset recommendation conditions, from the candidate investment strategies; and sending the target investment strategy as recommendation information to the client so as to enable the client to display the recommendation information.
7. The method according to claim 6, wherein the obtaining historical trading scenario information in a first historical time period, and based on the historical trading scenario information, calculating first trading information corresponding to the candidate investment strategy comprises:
acquiring product information of each investment product in a historical trading scene in the first historical time period as first product information;
determining each investment product matched with the screening condition in the candidate investment strategy from the investment products in the historical trading scene in the first historical time period as a first investment product;
calculating transaction information which accords with the target evaluation index when investment is carried out according to the candidate investment strategy in the first historical time period based on the transaction mode in the candidate investment strategy and the first product information of the first investment product, and taking the transaction information as first transaction information;
the obtaining of the second transaction information corresponding to the second historical time period corresponding to the candidate investment strategy includes:
acquiring product information of each investment product in a historical trading scene in the second historical time period as second product information;
determining each investment product matched with the screening condition in the candidate investment strategy from each investment product in the historical trading scene in the second historical time period as a second investment product;
and in the second historical time period, performing simulated transaction based on the transaction mode in the candidate investment strategy and the second product information of the second investment product to obtain transaction information which meets the target evaluation index and is used as second transaction information.
8. The method according to claim 6, wherein the investment evaluation value acquisition request carries a plurality of target evaluation indexes;
the calculating the target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information comprises:
aiming at each target evaluation index, calculating an index value of the target evaluation index as a first index value based on first transaction information conforming to the target evaluation index;
calculating a weighted sum of first index values of the target evaluation indexes as a first investment evaluation value;
calculating an index value of the target evaluation index as a second index value based on the second transaction information conforming to the target evaluation index;
calculating the weighted sum of the second index values of the target evaluation indexes to serve as a second investment evaluation value;
and calculating the weighted sum of the first investment evaluation value and the second investment evaluation value to obtain a target investment evaluation value of the candidate investment strategy.
9. An information recommendation system, characterized in that the recommendation system comprises: a client and a recommendation server; the recommendation server is in communication connection with the transaction server;
the client is used for sending an information recommendation request containing configuration information of a target user identifier and information to be recommended to the recommendation server;
the recommendation server is used for receiving the information recommendation request; under the condition that the information to be recommended is determined to be the investment strategy based on the configuration information, obtaining a plurality of investment strategies to be recommended based on the configuration information; sending a corresponding user characteristic acquisition request to the transaction server based on the target user identification;
the transaction server is used for obtaining the user characteristics of the target user based on the historical investment data of the target user and returning the user characteristics to the recommendation server;
the recommendation server is also used for sending a corresponding product characteristic acquisition request to the trading server aiming at each investment strategy to be recommended;
the transaction server is also used for obtaining the product characteristics of the investment product corresponding to the investment strategy to be recommended and returning the product characteristics to the recommendation server;
the recommendation server is further configured to calculate similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user respectively, and the similarity is used as the similarity between the investment strategy to be recommended and the target user; acquiring an investment strategy to be recommended, of which the similarity with the target user meets a preset selection condition, and taking the investment strategy as a candidate investment strategy; sending a corresponding investment evaluation value acquisition request to the transaction server aiming at each candidate investment strategy;
the transaction server is further used for obtaining historical transaction scene information in a first historical time period according to the investment evaluation value obtaining request, and calculating first transaction information corresponding to the candidate investment strategy based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server;
the recommendation server is further used for determining a target investment strategy of which the corresponding target investment evaluation value meets preset recommendation conditions from the candidate investment strategies; sending the target investment strategy as recommendation information to the client;
the client is further used for displaying the recommendation information.
10. A recommendation server, characterized in that the recommendation server comprises:
the information recommendation request receiving module is used for receiving an information recommendation request sent by the client; the information recommendation request comprises a target user identifier and configuration information of information to be recommended;
the to-be-recommended investment strategy acquisition module is used for acquiring a plurality of to-be-recommended investment strategies based on the configuration information under the condition that the to-be-recommended information is determined to be an investment strategy based on the configuration information;
the user characteristic acquisition module is used for sending a corresponding user characteristic acquisition request to the transaction server based on the target user identification; so that the transaction server obtains the user characteristics of the target user based on the transaction data of the historical investment of the target user and returns the user characteristics to the recommendation server;
the product characteristic acquisition module is used for sending a corresponding product characteristic acquisition request to the transaction server aiming at each investment strategy to be recommended; so that the trading server obtains the product characteristics of the investment product corresponding to the investment strategy to be recommended and returns the product characteristics to the recommending server;
the similarity calculation module is used for calculating the similarity between the product characteristics corresponding to each investment strategy to be recommended and the user characteristics of the target user respectively, and the similarity is used as the similarity between the investment strategy to be recommended and the target user;
the candidate investment strategy acquisition module is used for acquiring an investment strategy to be recommended, the similarity of which with the target user meets preset selection conditions, and the investment strategy to be recommended is used as a candidate investment strategy;
the investment evaluation value acquisition module is used for sending a corresponding investment evaluation value acquisition request to the trading server aiming at each candidate investment strategy; the transaction server obtains a request aiming at the investment evaluation value, historical transaction scene information in a first historical time period is obtained, and first transaction information corresponding to the candidate investment strategy is calculated based on the historical transaction scene information; the first historical time period is a time period earlier than the time when the information recommendation request is received; and obtaining second transaction information corresponding to a second historical time period corresponding to the candidate investment strategy; wherein the second historical time period is a time period for conducting a simulated transaction based on the candidate investment strategy; calculating a target investment evaluation value of the candidate investment strategy based on the first transaction information and the second transaction information, and returning the target investment evaluation value to the recommendation server;
the target investment strategy acquisition module is used for determining a target investment strategy of which the corresponding target investment evaluation value meets a preset recommendation condition from the candidate investment strategies;
and the recommendation information output module is used for sending the target investment strategy as recommendation information to the client so as to enable the client to display the recommendation information.
CN202210524498.6A 2022-05-13 2022-05-13 Information recommendation method and system and recommendation server Pending CN114943582A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115795072A (en) * 2023-02-03 2023-03-14 北京数慧时空信息技术有限公司 Dynamic mixing recommendation system and method for remote sensing image
CN117992676A (en) * 2024-04-02 2024-05-07 福建省君诺科技成果转化服务有限公司 Intelligent scientific and technological achievement recommendation method based on big data

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115795072A (en) * 2023-02-03 2023-03-14 北京数慧时空信息技术有限公司 Dynamic mixing recommendation system and method for remote sensing image
CN117992676A (en) * 2024-04-02 2024-05-07 福建省君诺科技成果转化服务有限公司 Intelligent scientific and technological achievement recommendation method based on big data
CN117992676B (en) * 2024-04-02 2024-06-07 福建省君诺科技成果转化服务有限公司 Intelligent scientific and technological achievement recommendation method based on big data

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