CN116881544A - Financial product information pushing method, device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a financial product information pushing method, a financial product information pushing device, computer equipment and a storage medium, and relates to the field of big data. The method comprises the steps of determining corresponding target financial product information from a plurality of financial product information according to label information of each user and behavior data of each user in each user group by acquiring a plurality of user groups and label information of each user in each user group, which are output by a label identification model, constructing a target product information database corresponding to each user group and containing the plurality of target financial product information, and pushing the target financial product information in the corresponding target product information database to the user of each user group. Compared with the traditional method for pushing information to users aiming at single products, the method and the device for pushing the financial product information determine group labels by combining the images of the user groups, and build product information databases of all groups, when the users use the financial application, corresponding financial product information is pushed to the users based on the product information databases, and therefore push comprehensiveness is improved.
Description
Technical Field
The present application relates to the field of big data technology, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for pushing information of a financial product.
Background
With the development of mobile device technology, users can browse and select financial products through mobile devices. The service range of the service provided by the financial products is wider, and the service range covers all aspects of life of users, and because more users use mobile equipment, it is very important to push the financial products which are more in line with the demands of the users for the mobile equipment of the users. The current way to push financial product information to a user's device is typically to push for a single product information. However, the types of financial products are various, and information is pushed to users aiming at single products, so that comprehensive consideration on the demands of the users is lacking.
Therefore, the current method for pushing information of financial products has the defect of low comprehensiveness.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a financial product information pushing method, apparatus, computer device, computer readable storage medium, and computer program product that can improve the push comprehensiveness.
In a first aspect, the present application provides a financial product information pushing method, the method including:
acquiring behavior data of a user aiming at financial application and acquiring user attribute information input by the user; the financial application comprises a plurality of financial product information;
inputting a plurality of user attribute information into a tag identification model, and obtaining the tag identification model to divide a plurality of user groups based on the plurality of user attribute information and then outputting the plurality of user groups and the tag information of each user in each user group;
for each user group, determining corresponding target financial product information from the plurality of financial product information according to label information of each user in the user group and behavior data of each user, and constructing a target product information database corresponding to the user group according to the target financial product information;
and pushing the corresponding target financial product information in the target product information database to the users of each user group.
In one embodiment, the obtaining the user attribute information input by the user includes:
acquiring industry information and age information input by a user as user identity information;
acquiring user financial preference information input by a user;
And obtaining the user attribute information according to the user identity information and the user financial preference information.
In one embodiment, the inputting the plurality of user attribute information into the tag identification model includes:
inputting user identity information and user financial preference information in a plurality of user attribute information into a tag identification model, dividing a plurality of user groups based on age information and industry information in the plurality of user identity information by the tag identification model, and determining first tag information of each user group;
generating corresponding second tag information based on the user financial preference information of each user in each user group, and outputting a plurality of user groups and the tag information of each user in each user group after determining the tag information of each user according to the first tag information and the second tag information.
In one embodiment, the generating the corresponding second tag information based on the user financial preference information of each user in each user group includes:
identifying target information in the user financial preference information according to a preset natural language algorithm; the target information comprises at least one of an entity, a preset keyword and a preset theme;
And generating corresponding second tag information according to the target information.
In one embodiment, the acquiring behavior data of the user for the financial application includes:
for each piece of financial product information in the financial application, if detecting that the user has interaction information on the financial product information, acquiring at least one of use information of the user on the financial product information, product acquisition information and activity participation information of the financial product information as sub-behavior data of the user on the financial product information;
and obtaining behavior data of the user for the financial application according to the sub-behavior data of the user for at least one financial product information in the financial application.
In one embodiment, the tag information includes location information;
for each user group, determining corresponding target financial product information from the plurality of financial product information according to the label information of each user in the user group and the behavior data of each user, including:
determining the position information of each user according to the label information of each user in each user group;
acquiring regional financial product information corresponding to the position information of each user in the user group from the plurality of financial product information according to the position information;
Acquiring at least one of use information of each user on financial product information of each region, product acquisition information of each user on financial product information of each region and activity participation information of each user on financial product information of each region in the user group;
determining the interaction times of the user group to the financial product information of each region according to at least one of the use information, the product acquisition information and the activity participation information;
and determining target financial product information corresponding to the label information of each user in the user group from financial product information of each region according to the comparison result of the interaction times and the preset interaction threshold.
In one embodiment, the method further comprises:
detecting registration information input by a new user in the financial application, and determining user attribute information of the new user according to the registration information;
inputting the user attribute information of the new user into the tag identification model to obtain the tag information of the new user output by the tag identification model;
and determining a target product information database associated with the new user according to the label information of the new user, and pushing the corresponding target financial product information in the target product information database to the new user.
In a second aspect, the present application provides a financial product information pushing apparatus, the apparatus comprising:
the acquisition module is used for acquiring behavior data of a user aiming at financial application and acquiring user attribute information input by the user; the financial application comprises a plurality of financial product information;
the identification module is used for inputting a plurality of user attribute information into the tag identification model, and acquiring the tag identification model, dividing a plurality of user groups based on the plurality of user attribute information, and outputting the plurality of user groups and tag information of each user in each user group;
the construction module is used for determining corresponding target financial product information from the plurality of financial product information according to the label information of each user in the user group and the behavior data of each user aiming at each user group, and constructing a target product information database corresponding to the user group according to the target financial product information;
and the pushing module is used for pushing the corresponding target financial product information in the target product information database to the users of each user group.
In a third aspect, the present application provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the processor executes the computer program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method described above.
In a fifth aspect, the application provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described above.
The financial product information pushing method, the device, the computer equipment, the storage medium and the computer program product are characterized in that a plurality of user attribute information input by a plurality of users is input into a tag identification model, the tag identification model is obtained, the plurality of user groups output after the plurality of user groups are divided based on the plurality of user attribute information and the tag information of each user in each user group, corresponding target financial product information is determined from the plurality of financial product information according to the tag information of each user and the behavior data of each user in each user group, a target product information database which corresponds to each user group and contains the plurality of target financial product information is constructed, and the corresponding target financial product information in the corresponding target product information database is pushed to the user of each user group. Compared with the traditional method for pushing information to users aiming at single products, the method and the device for pushing the financial product information determine group labels by combining the images of the user groups, and build product information databases of all groups, when the users use the financial application, corresponding financial product information is pushed to the users based on the product information databases, and therefore push comprehensiveness is improved.
Drawings
FIG. 1 is an application environment diagram of a financial product information pushing method in one embodiment;
FIG. 2 is a flowchart of a financial product information pushing method according to an embodiment;
FIG. 3 is a flow chart illustrating the steps performed for data acquisition in one embodiment;
FIG. 4 is a block diagram of a financial product information pushing apparatus according to one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The financial product information pushing method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The user may use and browse the financial application through the terminal 102 and input corresponding user attribute information, the terminal 102 sends the user attribute information and the behavior data to the server 104, the server 104 may construct a target product information database based on the behavior data and the user attribute information, and push target financial product information to the terminal 102 of the user of the corresponding group based on the target product information database, and the terminal 102 may display the received target financial product information in the financial application. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, among others. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, a financial product information pushing method is provided, and the method is applied to the server in fig. 1 for illustration, and includes the following steps:
step S202, acquiring behavior data of a user aiming at financial application and acquiring user attribute information input by the user; the financial application includes a plurality of financial product information.
The financial application may be a software application in the financial domain, such as a financial applet, among others. Financial application includes financial product information for a plurality of financial products. The plurality of financial product information may encompass a plurality of categories such as sports, entertainment, medical, education, senior citizens, and the like. Different kinds of financial product information are suitable for different people. The server may also obtain user attribute information entered by the user. For example, the server may cause the server to present an attribute input interface where a user may input user attribute information. Wherein the user attribute information represents static information of the user, such as identity information of the user, preference information of the user, and the like. Users who use financial applications cover various industries, various age groups. Different user groups have different use preferences, so the server can push financial product information conforming to the user preferences for the user based on the attribute information and the behavior data of the user by acquiring the behavior data of the user in the financial application and the attribute information of the user.
Step S204, inputting the plurality of user attribute information into a tag recognition model, and obtaining the tag recognition model to divide the plurality of user groups based on the plurality of user attribute information and then outputting the plurality of user groups and the tag information of each user in each user group.
The server may perform iterative training in advance based on a plurality of user attribute information samples and tag information samples corresponding to the plurality of user attribute information samples to obtain a tag identification model. For example, the server inputs a plurality of user attribute information samples into a tag recognition model to be trained, acquires predicted tag information of each user based on the user attribute information output by the tag recognition model to be trained, and adjusts model parameters of the tag recognition model to be trained based on a similarity comparison result of the predicted tag information of each user and the tag information sample corresponding to the user until a preset training end condition is met, so as to obtain the trained tag recognition model. The preset training ending condition may be that the difference value of the similarity is smaller than a preset similarity threshold value within a preset training frequency, or the training frequency reaches the preset training frequency.
The server may input the obtained plurality of user attribute information into the tag recognition model, obtain the tag recognition model, divide a plurality of user groups based on the plurality of user attributes, and output the plurality of user groups and tag information recognized for each user in each user group, that is, the server may construct a user portrait according to user attribute information of users using a financial application, divide the user groups, and set tag information of each user in each group. The tag information of the users in each user group can be various. For example, the user attribute information may include occupation, age, preference of financial products, location information of the user, and the like, and the server may cluster attribute information of the same kind through a tag recognition model, and perform tag recognition on a plurality of user groups obtained after the clustering, for example, determine tag information of each user group based on attribute information of which the number in each user group is greater than a preset value.
Step S206, for each user group, determining corresponding target financial product information from a plurality of financial product information according to the label information of each user in the user group and the behavior data of each user, and constructing a target product information database corresponding to the user group according to the target financial product information.
The server can acquire behavior data of each user in the financial application, and determine the interest degree of each user group in each financial product information in the financial application based on the behavior data of the user, so that the server can determine target financial product information of interest of each user group from a plurality of financial product information. The behavior data of the user in the financial application can be various, such as information of use, browsing, activity participation and the like; the target financial product information may be a plurality of, for example, related functions, products, activities, etc. in a financial application. The server may construct a target product information database corresponding to each user group based on the plurality of target financial product information. The server can correlate the label information of each user group with the target product information database of the corresponding user group.
Step S208, pushing the corresponding target financial product information in the target product information database to the users of each user group.
The server builds a target financial product information database of each user group, associates the target financial product information database with the label information of the corresponding user group, and then pushes the corresponding target financial product information in the target product information database to the users of each user group. For example, the server may identify tag information of each user in each user group, determine an associated target financial product information database based on the tag information, and further obtain corresponding target financial product information from the target financial product information database, and push the plurality of target financial product information to a financial application in the user terminal.
In some embodiments, the server may further obtain user attribute information of the new user, and determine a tag of the new user according to the user attribute information of the new user, and push corresponding financial product information. For example, in one embodiment, the server may obtain registration information entered by the new user in the financial application when the registration information is detected, and determine user attribute information of the new user according to the registration information; the server can input the user attribute information of the new user into the tag identification model, and the tag identification model determines the tag information of the new user based on the user attribute information, so that the server can acquire the tag information of the new user output by the tag identification model; and determining a target product information database associated with the new user according to the label information of the new user, and pushing the corresponding target financial product information in the target product information database to the new user.
In addition, in some embodiments, the server may further obtain behavior data of the newly added user in the financial application, and combine the steps of determining user tag information based on the tag identification model and the user attribute information, and determining target financial product information of interest to the user based on the behavior data, to update the user portraits of the user groups, that is, to update the tag information of the user groups; and updating the target financial product information in the target product information database associated with the label information of each user group, so that the financial product information pushed to each user group is more accurate and meets the requirements of users.
In the financial product information pushing method, a plurality of user attribute information input by a plurality of users is input into a tag identification model, the tag identification model is obtained, the plurality of user groups output after the plurality of user groups are divided based on the plurality of user attribute information, and the tag information of each user in each user group is obtained, corresponding target financial product information is determined from the plurality of financial product information according to the tag information of each user and the behavior data of each user in each user group, a target product information database which corresponds to each user group and contains the plurality of target financial product information is constructed, and the target financial product information in the corresponding target product information database is pushed to the user of each user group. Compared with the traditional method for pushing information to users aiming at single products, the method and the device for pushing the financial product information determine group labels by combining the images of the user groups, and build product information databases of all groups, when the users use the financial application, corresponding financial product information is pushed to the users based on the product information databases, and therefore push comprehensiveness is improved.
In one embodiment, obtaining user attribute information entered by a user includes: acquiring industry information and age information input by a user as user identity information; acquiring user financial preference information input by a user; and obtaining the user attribute information according to the user identity information and the user financial preference information.
In this embodiment, the server may obtain multiple kinds of attribute information input by the user, and it should be noted that the user inputs the user attribute information under the condition that the user knows that the user attribute information is used for pushing the product. The user attribute information may include identity information of the user, financial preference information of the user, and the like. The user identity information characterizes the age, industry and the like of the user, and the user financial preference information represents information of interest to the user in the financial field. After the user can input the user attribute information in the financial application of the terminal, the terminal sends the user attribute information to the server, the server can acquire the industry information and the age information input by the user as user identity information and acquire the financial preference information input by the user, and accordingly the server can acquire the user attribute information according to the user identity information and the user financial preference information. The industry information may include, among other things, the user's occupation, and the user's job site. The financial preference information may include user preferences for various parts of the financial field, such as whether stock is preferred or funding is preferred.
Through the embodiment, the server can acquire various user attribute information, further determine tag information based on the various user attribute information and push target financial product information, and the comprehensiveness of pushing the financial product information is improved.
In one embodiment, inputting a plurality of user attribute information into a tag identification model includes: inputting user identity information and user financial preference information in the plurality of user attribute information into a tag identification model, dividing a plurality of user groups by the tag identification model based on age information and industry information in the plurality of user identity information, and determining first tag information of each user group; generating corresponding second tag information based on the user financial preference information of each user in each user group, and outputting a plurality of user groups and the tag information of each user in each user group after determining the tag information of each user according to the first tag information and the second tag information.
In this embodiment, the server may input the acquired plurality of user attribute information into the tag identification model, specifically may input the plurality of user identity information and the plurality of user financial preference information into the tag identification model, and the tag identification model may divide a plurality of user groups based on age information and industry information in the plurality of user identity information, and determine first tag information of each user group, where the first tag information may be determined based on the age information and the industry information. The server may also generate second tag information corresponding to each user in each user group based on the user financial preference information of each user in each user group, where the second tag information may be tag information that is more refined relative to the first tag information. The tag recognition model may determine tag information of each user according to the first tag information and the second tag information, for example, combine the first tag information and the second tag information, so that the tag recognition model may output tag information of a plurality of user groups and users in each user group.
Wherein the tag identification model may determine the second tag information based on an algorithm. For example, in one embodiment, when the tag recognition model generates the second tag recognition model based on the user financial preference information of each user in each user group, the target information in the user financial preference information may be recognized according to a preset natural language algorithm. The target information comprises at least one of an entity, a preset keyword and a preset theme. That is, the tag recognition model may extract information such as entities, keywords, topics, etc. in the financial preference information, so that the server may generate corresponding second tag information according to the entities, keywords, topics, etc. in the target information by the tag recognition model.
Specifically, the tag information determination process may be a process of constructing a user portrait. The server may complete hierarchical clustering of users based on the user attribute information. For example, the server may set a plurality of age groups, and layer the user based on age information of the user to obtain users of the plurality of age groups; and then grouping based on the industry information of the users to obtain user groups corresponding to different industries in each age group. The server can perform manual and algorithmic labeling on the user based on the rules at the same time. When the label recognition model is used, the label recognition model can construct more refined labels for users to supplement through the modes of entity recognition, keyword extraction, theme extraction and the like based on a natural language algorithm. For example, the tag recognition model recognizes target information such as entities, keywords, and subjects in the above-described financial preference information based on a natural language algorithm, and further determines second tag information based on the recognized target information as more refined tag information. In some embodiments, the server may further obtain tag information manually determined based on the attribute information of the user, so that the server may combine the tags generated by the manual and the algorithm to complete the division of the user groups and the determination of the tag information in each user group.
Through the embodiment, the server can determine the label information of the user based on various information in the user attribute information by the label identification model, so that the division of the user groups and the determination of the label information are realized, and the corresponding financial product information is pushed for the users of each user group based on the label information, so that the comprehensiveness of the financial product pushing is improved.
In one embodiment, as shown in fig. 3, fig. 3 is a flow chart illustrating the steps of data acquisition in one embodiment. Acquiring behavior data of a user for a financial application, including: step S302, aiming at each piece of financial product information in the financial application, if detecting that the user has interaction information on the financial product information, acquiring at least one of use information of the user on the financial product information, product acquisition information and activity participation information of the financial product information as sub-behavior data of the user on the financial product information; step S304, according to the sub-behavior data of the user on at least one piece of financial product information in the financial application, the behavior data of the user on the financial application is obtained.
In this embodiment, the server may obtain behavior data of the user in the financial application. Wherein the financial applications variously contain a plurality of financial product information, for each of which the server can detect whether the user has interacted with the financial product information. If the server detects that the interaction information exists on the financial product information by the user, the server can acquire at least one of the use information, the product acquisition information and the activity participation information of the financial product by the user as the sub-behavior data of the financial product information by the user. The server can obtain multiple pieces of sub-behavior data of the user on the financial product information, so that the server can obtain behavior data of the user on the financial application according to the sub-behavior data of the user on at least one piece of financial product information in the financial application.
Specifically, the server may build a target marketing information base for a particular group by analyzing user behavior data for that group. For example, for each user group, the server obtains sub-behavior data of each user of the user group for each financial product information in the financial application, such as information about use condition, purchase condition, activity participation condition, and the like of each function in the financial application. Wherein the product acquisition information may be referred to as a purchase condition. The server can identify group characteristics based on the behavior data of the users, such as financial product information with the interaction times corresponding to each user group being greater than a preset interaction times threshold, determine group characteristics of each user group based on the times of the user performing actions such as using, purchasing, participating in activities and the like in the financial product information, and determine label information of each user group based on the group characteristics.
According to the embodiment, the server can determine the label information of each user in each user group based on the behavior data of the users such as the use, purchase, activity participation and the like of the financial products, so that the target financial product information pushed to each user is determined based on the label information, and the push comprehensiveness of the financial products is improved.
In one embodiment, for each user group, determining corresponding target financial product information from a plurality of financial product information according to tag information of each user in the user group and behavior data of each user, includes: determining the position information of each user according to the label information of each user in each user group; acquiring regional financial product information corresponding to the position information of each user in the user group from a plurality of financial product information according to the position information; acquiring at least one of use information of each user on financial product information of each region, product acquisition information of each user on financial product information of each region and activity participation information of each user on financial product information of each region in the user group; determining the interaction times of the user group to the financial product information of each region according to at least one of the use information, the product acquisition information and the activity participation information; and determining target financial product information corresponding to the label information of each user in the user group from financial product information of each region according to the comparison result of the interaction times and the preset interaction threshold value.
In this embodiment, the tag information may further include location information, and the location information may be included in attribute information of the user. For users in different geographic locations, the surrounding financial product resources will be different, so the server can push the target financial product information in combination with the location information of each user group. For example, for each user group, the server may determine location information for each user based on tag information for each user in the user group. The financial product information in the above-mentioned financial application may also correspond to location information, such as a use location, a purchase location, an activity location, etc. of the financial product information.
The server may obtain regional financial product information corresponding to the location information of each user in the user group from the plurality of financial product information according to the location information of the user. The server can acquire at least one of practical information, product acquisition information and activity participation information of financial product information corresponding to the position information of each user in the group. The server can determine the interaction times of the user group to the financial product information of each region according to at least one of the use information, the product acquisition information and the activity participation information. The server may compare the interaction times with a preset interaction threshold, and determine, according to a comparison result of the comparison, target financial product information corresponding to the tag information of each user in the user group from financial product information of each region.
Specifically, the server may extract target financial product information with the interaction times greater than or equal to a preset interaction threshold, and associate the extracted target financial product information to the user group corresponding to the position information according to the position information in the label information of each user in each user group, so as to achieve association between the target financial product information and the label information of the user group. And the server can push target financial product information which accords with the position information and the preference information in the label information of each user in the associated user group, such as information of functions, products, activities and the like in financial application, based on the target product information database constructed by the target financial product information.
According to the embodiment, the server can combine behavior data of the user on financial product information in financial application and position information of the user to determine target financial product information which accords with user preference and position of the user in groups for each user group, so that comprehensiveness of financial product information pushing is improved; and the server completes the user portrait by combining the manual label and the natural language algorithm, so that the user portrait result is more accurate. The target product information database is produced based on user group behaviors, functions, products, activities and the like in financial application are covered, the comprehensive and accurate pushing from service to products is realized, the user demand preference is fitted, the attraction of more users is facilitated, the user range is enlarged, and the user activity and the user viscosity are improved. Meanwhile, the whole flow is realized based on an algorithm and a model, so that the result is more convincing.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a financial product information pushing device for realizing the above related financial product information pushing method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the financial product information pushing device or devices provided below may refer to the limitation of the financial product information pushing method hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 4, there is provided a financial product information pushing apparatus, comprising: an acquisition module 500, an identification module 502, a construction module 504, and a push module 506, wherein:
the acquiring module 500 is configured to acquire behavior data of a user for a financial application and acquire user attribute information input by the user; the financial application includes a plurality of financial product information.
The identification module 502 is configured to input the plurality of user attribute information into the tag identification model, obtain the tag identification model, divide the plurality of user groups based on the plurality of user attribute information, and output the plurality of user groups and the tag information of each user in each user group.
The construction module 504 is configured to determine, for each user group, corresponding target financial product information from the plurality of financial product information according to tag information of each user in the user group and behavior data of each user, and construct a target product information database corresponding to the user group according to the target financial product information.
And the pushing module 506 is configured to push the target financial product information in the corresponding target product information database to the users of each user group.
In one embodiment, the obtaining module 500 is specifically configured to obtain, as the user identity information, industry information and age information input by a user; acquiring user financial preference information input by a user; and obtaining the user attribute information according to the user identity information and the user financial preference information.
In one embodiment, the identification module 502 is specifically configured to input user identity information and user financial preference information in a plurality of user attribute information into a tag identification model, and divide a plurality of user groups and determine first tag information of each user group based on age information and industry information in the plurality of user identity information by the tag identification model; generating corresponding second tag information based on the user financial preference information of each user in each user group, and outputting a plurality of user groups and the tag information of each user in each user group after determining the tag information of each user according to the first tag information and the second tag information.
In one embodiment, the identifying module 502 is specifically configured to identify target information in the financial preference information of the user according to a preset natural language algorithm; the target information comprises at least one of an entity, a preset keyword and a preset theme; and generating corresponding second tag information according to the target information.
In one embodiment, the obtaining module 500 is specifically configured to obtain, for each piece of financial product information in the financial application, at least one of usage information of the financial product information, product obtaining information, and activity participation information of the financial product information of the user as sub-behavior data of the user on the financial product information if it is detected that the user has interactive information on the financial product information; and obtaining behavior data of the user for the financial application according to the sub-behavior data of the user for at least one financial product information in the financial application.
In one embodiment, the building module 504 is specifically configured to determine, for each user group, location information of each user according to tag information of each user in the user group; acquiring regional financial product information corresponding to the position information of each user in the user group from a plurality of financial product information according to the position information; acquiring at least one of use information of each user on financial product information of each region, product acquisition information of each user on financial product information of each region and activity participation information of each user on financial product information of each region in the user group; determining the interaction times of the user group to the financial product information of each region according to at least one of the use information, the product acquisition information and the activity participation information; and determining target financial product information corresponding to the label information of each user in the user group from financial product information of each region according to the comparison result of the interaction times and the preset interaction threshold value.
In one embodiment, the apparatus further comprises: the updating module is used for detecting registration information input by a new user in the financial application and determining user attribute information of the new user according to the registration information; inputting the user attribute information of the new user into the tag identification model, and obtaining the tag information of the new user output by the tag identification model; and determining a target product information database associated with the new user according to the label information of the new user, and pushing the corresponding target financial product information in the target product information database to the new user.
The above-mentioned various modules in the financial product information pushing apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing financial product information data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a financial product information pushing method.
It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided, including a memory and a processor, where the memory stores a computer program, and the processor implements the financial product information pushing method described above when executing the computer program.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the above-described financial product information pushing method.
In one embodiment, a computer program product is provided, including a computer program that when executed by a processor implements the financial product information pushing method described above.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (11)
1. A financial product information pushing method, the method comprising:
acquiring behavior data of a user aiming at financial application and acquiring user attribute information input by the user; the financial application comprises a plurality of financial product information;
inputting a plurality of user attribute information into a tag identification model, and obtaining the tag identification model to divide a plurality of user groups based on the plurality of user attribute information and then outputting the plurality of user groups and the tag information of each user in each user group;
For each user group, determining corresponding target financial product information from the plurality of financial product information according to label information of each user in the user group and behavior data of each user, and constructing a target product information database corresponding to the user group according to the target financial product information;
and pushing the corresponding target financial product information in the target product information database to the users of each user group.
2. The method of claim 1, wherein the obtaining user attribute information entered by a user comprises:
acquiring industry information and age information input by a user as user identity information;
acquiring user financial preference information input by a user;
and obtaining the user attribute information according to the user identity information and the user financial preference information.
3. The method of claim 2, wherein said entering a plurality of user attribute information into a tag identification model comprises:
inputting user identity information and user financial preference information in a plurality of user attribute information into a tag identification model, dividing a plurality of user groups based on age information and industry information in the plurality of user identity information by the tag identification model, and determining first tag information of each user group;
Generating corresponding second tag information based on the user financial preference information of each user in each user group, and outputting a plurality of user groups and the tag information of each user in each user group after determining the tag information of each user according to the first tag information and the second tag information.
4. The method of claim 3, wherein generating the corresponding second tag information based on the user financial preference information of each user in each user group comprises:
identifying target information in the user financial preference information according to a preset natural language algorithm; the target information comprises at least one of an entity, a preset keyword and a preset theme;
and generating corresponding second tag information according to the target information.
5. The method of claim 1, wherein the obtaining behavior data of the user for the financial application comprises:
for each piece of financial product information in the financial application, if detecting that the user has interaction information on the financial product information, acquiring at least one of use information of the user on the financial product information, product acquisition information and activity participation information of the financial product information as sub-behavior data of the user on the financial product information;
And obtaining behavior data of the user for the financial application according to the sub-behavior data of the user for at least one financial product information in the financial application.
6. The method of claim 5, wherein the tag information comprises location information;
for each user group, determining corresponding target financial product information from the plurality of financial product information according to the label information of each user in the user group and the behavior data of each user, including:
determining the position information of each user according to the label information of each user in each user group;
acquiring regional financial product information corresponding to the position information of each user in the user group from the plurality of financial product information according to the position information;
acquiring at least one of use information of each user on financial product information of each region, product acquisition information of each user on financial product information of each region and activity participation information of each user on financial product information of each region in the user group;
determining the interaction times of the user group to the financial product information of each region according to at least one of the use information, the product acquisition information and the activity participation information;
And determining target financial product information corresponding to the label information of each user in the user group from financial product information of each region according to the comparison result of the interaction times and the preset interaction threshold.
7. The method according to claim 1, wherein the method further comprises:
detecting registration information input by a new user in the financial application, and determining user attribute information of the new user according to the registration information;
inputting the user attribute information of the new user into the tag identification model to obtain the tag information of the new user output by the tag identification model;
and determining a target product information database associated with the new user according to the label information of the new user, and pushing the corresponding target financial product information in the target product information database to the new user.
8. A financial product information pushing apparatus, the apparatus comprising:
the acquisition module is used for acquiring behavior data of a user aiming at financial application and acquiring user attribute information input by the user; the financial application comprises a plurality of financial product information;
the identification module is used for inputting a plurality of user attribute information into the tag identification model, and acquiring the tag identification model, dividing a plurality of user groups based on the plurality of user attribute information, and outputting the plurality of user groups and tag information of each user in each user group;
The construction module is used for determining corresponding target financial product information from the plurality of financial product information according to the label information of each user in the user group and the behavior data of each user aiming at each user group, and constructing a target product information database corresponding to the user group according to the target financial product information;
and the pushing module is used for pushing the corresponding target financial product information in the target product information database to the users of each user group.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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| CN202310657992.4A CN116881544A (en) | 2023-06-05 | 2023-06-05 | Financial product information pushing method, device, computer equipment and storage medium |
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| CN202310657992.4A CN116881544A (en) | 2023-06-05 | 2023-06-05 | Financial product information pushing method, device, computer equipment and storage medium |
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