CN116911950A - Electronic commerce information push system, terminal and equipment based on artificial intelligence - Google Patents
Electronic commerce information push system, terminal and equipment based on artificial intelligence Download PDFInfo
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Abstract
The invention belongs to the technical field of electronic commerce information processing, and discloses an electronic commerce information pushing system, a terminal and equipment based on artificial intelligence, wherein the electronic commerce information pushing system comprises the following components: the system comprises a user information acquisition module, a historical data acquisition module, a data preprocessing module, a central control module, a commodity information analysis module, a user behavior analysis module, a user motivation analysis module, a scene analysis module, a user portrait generation module, a recommendation information generation module, a pushing module and a feedback acquisition result and updating module. The invention can effectively analyze the user data of the electronic commerce platform, accurately recommend the goods or services required by the client to the client according to the shopping or browsing and other requirements of the user, can meet the shopping or entertainment requirements of the user, improve the experience and satisfaction of the user, reduce the shopping difficulty of the user and improve the operation efficiency of the electronic commerce platform.
Description
Technical Field
The invention belongs to the technical field of electronic commerce information processing, and particularly relates to an electronic commerce information pushing system, terminal and equipment based on artificial intelligence.
Background
At present, electronic commerce commodity information is pushed, namely a merchant utilizes an electronic commerce platform to acquire and integrate valuable commodity information and other contents from a commodity information database according to the requirements of customers, a pushing method and technology are used for transmitting information, timely and active information service is provided for the customers, and the purposes of transmitting the proper commodity information and other contents to the customers with the requirements at proper time and proper places in proper modes are achieved, so that the customers are assisted in making effective purchase decisions. Through the electronic commerce commodity information pushing, the time cost for acquiring the commodity information and other contents is saved for the clients, and meanwhile, the service cost is saved for electronic commerce merchants and more profits are brought. However, the user portrait generated by the existing e-commerce information pushing method is inaccurate; meanwhile, the pure recommendation based on the interests of the user cannot meet the requirements of the user, and the push pertinence is not strong.
Through the above analysis, the problems and defects existing in the prior art are as follows: the user portrait generated by the existing electronic commerce information pushing method is inaccurate; meanwhile, the pure recommendation based on the interests of the user cannot meet the requirements of the user, and the push pertinence is not strong.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides an electronic commerce information pushing system, a terminal and equipment based on artificial intelligence.
The invention is realized in such a way that an electronic commerce information pushing system based on artificial intelligence comprises:
the user behavior analysis module is connected with the central control module and used for analyzing behavior characteristics, consumption preference characteristics and consumption reasons of the user based on the preprocessed data;
the user motivation analysis module is connected with the central control module and used for analyzing the login motivation of the user based on the preprocessed information and the recently browsed data of the user;
the scene analysis module is connected with the central control module and used for analyzing the scene of the user when the user consumes based on the preprocessed shopping data and browsing data of the user, the analysis result of the user behavior and the motivation analysis result;
the user portrait generation module is connected with the central control module and is used for generating a user portrait based on the basic information of the user and the analysis result of the user behavior;
the recommendation information generation module is connected with the central control module and is used for generating electronic commerce information to be pushed based on subscription, attention information, user behavior analysis results, motivation analysis results, scene analysis results, commodity information analysis results and generated user portraits of a user;
the pushing module is connected with the central control module and used for pushing the electronic commerce information to be pushed to the display page;
and the feedback acquisition result is connected with the central control module and is used for acquiring the feedback of the user on pushing based on the operation of the user on pushing information, the browsing duration and the follow-up shopping behavior.
Further, the electronic commerce information pushing system based on artificial intelligence further comprises:
the user information acquisition module is connected with the central control module and is used for acquiring age, height and other basic information of a user; simultaneously acquiring subscription and attention information of a user;
the historical data acquisition module is connected with the central control module and is used for acquiring historical shopping data, historical browsing data and search data of a user by utilizing a crawler technology;
the data preprocessing module is connected with the central control module and is used for preprocessing the collected relevant data of the user;
the central control module is connected with the user information acquisition module, the historical data acquisition module, the data preprocessing module, the commodity information analysis module, the user behavior analysis module, the user motivation analysis module, the scene analysis module, the user portrait generation module, the recommendation information generation module, the pushing module, the feedback acquisition result and the updating module and is used for controlling each module to work normally by utilizing the singlechip or the controller;
the commodity information analysis module is connected with the central control module and is used for according to the association relation between commodities in the commodity database;
and the updating module is connected with the central control module and is used for updating the user portrait, the corresponding analysis result and the generated recommendation information based on the feedback of the user on pushing.
Further, the historical data acquisition module acquiring historical browsing data and searching data by utilizing a crawler technology comprises:
historical browsing data and search data of a user are collected by using the following steps:
wherein δ represents the user's historical browsing data and search data; i represents a data attribute; u represents a data class; a represents a data source; x represents the data amount; w represents the number of crawlers.
Further, the user behavior analysis module analyzes consumption preference characteristics of the user based on the preprocessed data, including:
firstly, extracting the times of historical shopping of a user and goods or services of each shopping based on preprocessed historical shopping data of the user;
secondly, classifying shopping behaviors according to the attribute of goods or services purchased by the user each time; and determining the number of times each type of shopping behavior occurs;
then, classifying and extracting keywords of all goods or services purchased by the user according to the goods or services purchased by the user each time and combining the shopping behaviors of the user;
and finally, determining a consumption preference feature set of the user according to the keywords of all the goods or services purchased by the user and the corresponding purchase times.
Further, the determining the consumption preference feature set of the user according to the keywords of all the goods or services purchased by the user and the corresponding purchase times comprises:
determining a plurality of consumption preference characteristics of the user according to keywords of all goods or services purchased by the user and corresponding purchase times;
judging whether the obtained multiple consumption preference characteristics of the user are short-term preferences, if so, judging whether the short-term consumption preference is a short-term preference in the near future, and if so, adding the corresponding consumption preference characteristics into a user consumption preference characteristic set; if the short-term consumption preference is not a short-term preference in the near future, deleting the corresponding consumption preference feature;
if the multiple consumption preference features of the user are not short-term preference, the corresponding consumption features are directly added into the user consumption preference feature set.
Further, the consumption reasons include: price, living necessities, interests, and others;
the motivations for the user to log in include: shopping consumption, browsing relaxation, knowledge searching, among others.
Further, the user behavior analysis module analyzes the consumption reasons of the user based on the preprocessed data, including:
firstly, acquiring commodities purchased by a user in historical shopping data, and determining the category of the commodities; simultaneously counting the proportion of commodities of each category in shopping of a user;
secondly, judging the usable time of the commodity according to the category of the commodity and the number of the commodity purchased by the user;
and finally, determining the times and the time of the purchase back of other users on the same type of commodity, and judging the consumption reason of the user based on the times and the time of the purchase back of the other users on the same type of commodity, the purchase frequency of the current user and the available time of the corresponding commodity.
It is a further object of the present invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to implement the artificial intelligence based e-commerce information pushing system.
It is another object of the present invention to provide a computer readable storage medium storing a computer program, which when executed by a processor, causes the processor to implement the artificial intelligence based e-commerce information pushing system.
Another object of the present invention is to provide an information data processing terminal for implementing the artificial intelligence-based electronic commerce information push system.
In combination with the above technical solution and the technical problems to be solved, please analyze the following aspects to provide the following advantages and positive effects:
first, aiming at the technical problems in the prior art and the difficulty in solving the problems, the technical problems solved by the technical proposal of the invention are analyzed in detail and deeply by tightly combining the technical proposal to be protected, the results and data in the research and development process, and the like, and some technical effects brought after the problems are solved have creative technical effects. The specific description is as follows:
the invention is not based on the interests of the user alone, but performs comprehensive analysis by combining the user portraits with the consumption psychology of the user and the user scene analysis, and combines the reverse matching of commodities and the shopping recommendation of similar users, thereby ensuring the accuracy and comprehensiveness of push information, and judging the demands of the user to perform accurate article or entertainment video push by analyzing the behavioral motivation of the user.
Secondly, the technical scheme is regarded as a whole or from the perspective of products, and the technical scheme to be protected has the following technical effects and advantages:
the invention can effectively analyze the user data of the electronic commerce platform, accurately recommend the goods or services required by the client to the client according to the shopping or browsing and other requirements of the user, can meet the shopping or entertainment requirements of the user, improve the experience and satisfaction of the user, reduce the shopping difficulty of the user and improve the operation efficiency of the electronic commerce platform.
Drawings
FIG. 1 is a schematic diagram of an e-commerce information pushing system based on artificial intelligence according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for a user behavior analysis module to analyze consumer preference characteristics of a user based on pre-processed data, provided by an embodiment of the present invention;
fig. 3 is a flowchart of a method for analyzing consumption reasons of a user by using a user behavior analysis module according to an embodiment of the present invention based on preprocessed data.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention 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 invention.
As shown in fig. 1, an electronic commerce information pushing system based on artificial intelligence according to an embodiment of the present invention includes:
the user information acquisition module is connected with the central control module and is used for acquiring age, height and other basic information of a user; simultaneously acquiring subscription and attention information of a user;
the historical data acquisition module is connected with the central control module and is used for acquiring historical shopping data, historical browsing data and search data of a user by utilizing a crawler technology;
the data preprocessing module is connected with the central control module and is used for preprocessing the collected relevant data of the user;
the central control module is connected with the user information acquisition module, the historical data acquisition module, the data preprocessing module, the commodity information analysis module, the user behavior analysis module, the user motivation analysis module, the scene analysis module, the user portrait generation module, the recommendation information generation module, the pushing module, the feedback acquisition result and the updating module and is used for controlling each module to work normally by utilizing the singlechip or the controller;
the commodity information analysis module is connected with the central control module and is used for according to the association relation between commodities in the commodity database;
the user behavior analysis module is connected with the central control module and used for analyzing behavior characteristics, consumption preference characteristics and consumption reasons of the user based on the preprocessed data;
the user motivation analysis module is connected with the central control module and used for analyzing the login motivation of the user based on the preprocessed information and the recently browsed data of the user;
the scene analysis module is connected with the central control module and used for analyzing the scene of the user when the user consumes based on the preprocessed shopping data and browsing data of the user, the analysis result of the user behavior and the motivation analysis result;
the user portrait generation module is connected with the central control module and is used for generating a user portrait based on the basic information of the user and the analysis result of the user behavior;
the recommendation information generation module is connected with the central control module and is used for generating electronic commerce information to be pushed based on subscription, attention information, user behavior analysis results, motivation analysis results, scene analysis results, commodity information analysis results and generated user portraits of a user;
the pushing module is connected with the central control module and used for pushing the electronic commerce information to be pushed to the display page;
the feedback acquisition result is connected with the central control module and is used for acquiring feedback of the user on pushing based on the operation of the user on pushing information, browsing duration and the follow-up shopping behavior;
and the updating module is connected with the central control module and is used for updating the user portrait, the corresponding analysis result and the generated recommendation information based on the feedback of the user on pushing.
The historical data acquisition module provided by the embodiment of the invention acquires historical browsing data and searching data by utilizing a crawler technology, and comprises the following steps:
historical browsing data and search data of a user are collected by using the following steps:
wherein δ represents the user's historical browsing data and search data; i represents a data attribute; u represents a data class; a represents a data source; x represents the data amount; w represents the number of crawlers.
As shown in fig. 2, the user behavior analysis module provided by the embodiment of the present invention analyzes consumption preference characteristics of a user based on preprocessed data, including:
s101, extracting the times of historical shopping of a user and goods or services of each shopping based on preprocessed historical shopping data of the user;
s102, classifying shopping behaviors according to the attribute of goods or services purchased by the user each time; and determining the number of times each type of shopping behavior occurs;
s103, classifying and extracting keywords of all goods or services purchased by the user according to the goods or services purchased by the user each time and combining the shopping behaviors of the user;
s104, determining a consumption preference feature set of the user according to the keywords of all goods or services purchased by the user and the corresponding purchase times.
The method for determining the consumption preference feature set of the user according to the keywords of all the goods or services purchased by the user and the corresponding purchase times comprises the following steps:
determining a plurality of consumption preference characteristics of the user according to keywords of all goods or services purchased by the user and corresponding purchase times;
judging whether the obtained multiple consumption preference characteristics of the user are short-term preferences, if so, judging whether the short-term consumption preference is a short-term preference in the near future, and if so, adding the corresponding consumption preference characteristics into a user consumption preference characteristic set; if the short-term consumption preference is not a short-term preference in the near future, deleting the corresponding consumption preference feature;
if the multiple consumption preference features of the user are not short-term preference, the corresponding consumption features are directly added into the user consumption preference feature set.
The consumption reasons provided by the embodiment of the invention include: price, living necessities, interests, and others.
The user login motivation provided by the embodiment of the invention comprises the following steps: shopping consumption, browsing relaxation, knowledge searching, among others.
As shown in fig. 3, the analysis module for user behavior provided in the embodiment of the present invention analyzes consumption reasons of a user based on preprocessed data, including:
s201, acquiring commodities purchased by a user in historical shopping data, and determining the category of the commodities; simultaneously counting the proportion of commodities of each category in shopping of a user; judging the usable time of the commodity according to the category of the commodity and the number of the commodity purchased by the user;
s202, determining the times and the time of the purchase back of other users on the same type of commodities, and judging the consumption reason of the user based on the times and the time of the purchase back of the other users on the same type of commodities, the purchase frequency of the current user and the available time of the corresponding commodities.
In order to prove the inventive and technical value of the technical solution of the present invention, this section is an application example on specific products or related technologies of the claim technical solution.
The invention applies the electronic commerce information pushing system based on the artificial intelligence to computer equipment, wherein the computer equipment comprises a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the electronic commerce information pushing system based on the artificial intelligence.
The invention applies the electronic commerce information pushing system based on the artificial intelligence to a computer readable storage medium, and stores a computer program which when executed by a processor, causes the processor to execute the electronic commerce information pushing system based on the artificial intelligence.
The electronic commerce information pushing system based on the artificial intelligence is applied to an information data processing terminal.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (10)
1. An electronic commerce information pushing system based on artificial intelligence, which is characterized in that the electronic commerce information pushing system based on artificial intelligence comprises:
the user behavior analysis module is connected with the central control module and used for analyzing behavior characteristics, consumption preference characteristics and consumption reasons of the user based on the preprocessed data;
the user motivation analysis module is connected with the central control module and used for analyzing the login motivation of the user based on the preprocessed information and the recently browsed data of the user;
the scene analysis module is connected with the central control module and used for analyzing the scene of the user when the user consumes based on the preprocessed shopping data and browsing data of the user, the analysis result of the user behavior and the motivation analysis result;
the user portrait generation module is connected with the central control module and is used for generating a user portrait based on the basic information of the user and the analysis result of the user behavior;
the recommendation information generation module is connected with the central control module and is used for generating electronic commerce information to be pushed based on subscription, attention information, user behavior analysis results, motivation analysis results, scene analysis results, commodity information analysis results and generated user portraits of a user;
the pushing module is connected with the central control module and used for pushing the electronic commerce information to be pushed to the display page;
and the feedback acquisition result is connected with the central control module and is used for acquiring the feedback of the user on pushing based on the operation of the user on pushing information, the browsing duration and the follow-up shopping behavior.
2. The artificial intelligence based e-commerce information pushing system of claim 1, further comprising:
the user information acquisition module is connected with the central control module and is used for acquiring age, height and other basic information of a user; simultaneously acquiring subscription and attention information of a user;
the historical data acquisition module is connected with the central control module and is used for acquiring historical shopping data, historical browsing data and search data of a user by utilizing a crawler technology;
the data preprocessing module is connected with the central control module and is used for preprocessing the collected relevant data of the user;
the central control module is connected with the user information acquisition module, the historical data acquisition module, the data preprocessing module, the commodity information analysis module, the user behavior analysis module, the user motivation analysis module, the scene analysis module, the user portrait generation module, the recommendation information generation module, the pushing module, the feedback acquisition result and the updating module and is used for controlling each module to work normally by utilizing the singlechip or the controller;
the commodity information analysis module is connected with the central control module and is used for according to the association relation between commodities in the commodity database;
and the updating module is connected with the central control module and is used for updating the user portrait, the corresponding analysis result and the generated recommendation information based on the feedback of the user on pushing.
3. The ecommerce information pushing system based on artificial intelligence of claim 2, wherein the historical data collection module obtaining historical browsing data and search data using crawler technology comprises:
historical browsing data and search data of a user are collected by using the following steps:
wherein δ represents the user's historical browsing data and search data; i represents a data attribute; u represents a data class; a represents a data source; x represents the data amount; w represents the number of crawlers.
4. The ecommerce information push system based on artificial intelligence of claim 1, wherein the user behavior analysis module analyzes consumer preference characteristics of a user based on the preprocessed data comprises:
firstly, extracting the times of historical shopping of a user and goods or services of each shopping based on preprocessed historical shopping data of the user;
secondly, classifying shopping behaviors according to the attribute of goods or services purchased by the user each time; and determining the number of times each type of shopping behavior occurs;
then, classifying and extracting keywords of all goods or services purchased by the user according to the goods or services purchased by the user each time and combining the shopping behaviors of the user;
and finally, determining a consumption preference feature set of the user according to the keywords of all the goods or services purchased by the user and the corresponding purchase times.
5. The ecommerce information push system based on artificial intelligence as recited in claim 4, wherein said determining a set of consumer preference characteristics for a user based on keywords of all goods or services purchased by the user and corresponding number of purchases comprises:
determining a plurality of consumption preference characteristics of the user according to keywords of all goods or services purchased by the user and corresponding purchase times;
judging whether the obtained multiple consumption preference characteristics of the user are short-term preferences, if so, judging whether the short-term consumption preference is a short-term preference in the near future, and if so, adding the corresponding consumption preference characteristics into a user consumption preference characteristic set; if the short-term consumption preference is not a short-term preference in the near future, deleting the corresponding consumption preference feature;
if the multiple consumption preference features of the user are not short-term preference, the corresponding consumption features are directly added into the user consumption preference feature set.
6. The ecommerce information push system based on artificial intelligence of claim 1, wherein the consumption reasons include: price, living necessities, interests, and others;
the motivations for the user to log in include: shopping consumption, browsing relaxation, knowledge searching, among others.
7. The ecommerce information push system based on artificial intelligence of claim 2, wherein the user behavior analysis module analyzes the consumption cause of the user based on the preprocessed data comprises:
firstly, acquiring commodities purchased by a user in historical shopping data, and determining the category of the commodities; simultaneously counting the proportion of commodities of each category in shopping of a user;
secondly, judging the usable time of the commodity according to the category of the commodity and the number of the commodity purchased by the user;
and finally, determining the times and the time of the purchase back of other users on the same type of commodity, and judging the consumption reason of the user based on the times and the time of the purchase back of the other users on the same type of commodity, the purchase frequency of the current user and the available time of the corresponding commodity.
8. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to implement the artificial intelligence based e-commerce information pushing system of any one of claims 1-7.
9. A computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to implement the artificial intelligence based e-commerce information pushing system of any one of claims 1 to 7.
10. An information data processing terminal, characterized in that the information data processing terminal is configured to implement the artificial intelligence based e-commerce information pushing system according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117391820A (en) * | 2023-12-01 | 2024-01-12 | 深圳市思迅网络科技有限公司 | SaaS service comprehensive management method and system |
| CN118096285A (en) * | 2023-12-12 | 2024-05-28 | 宁波杉路网络科技有限公司 | Electronic commerce user behavior analysis system based on big data |
-
2023
- 2023-07-27 CN CN202310938093.1A patent/CN116911950A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117391820A (en) * | 2023-12-01 | 2024-01-12 | 深圳市思迅网络科技有限公司 | SaaS service comprehensive management method and system |
| CN118096285A (en) * | 2023-12-12 | 2024-05-28 | 宁波杉路网络科技有限公司 | Electronic commerce user behavior analysis system based on big data |
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