WO2022089190A1 - Procédé et appareil de recommandation de produit, ainsi que dispositif électronique et support de stockage - Google Patents

Procédé et appareil de recommandation de produit, ainsi que dispositif électronique et support de stockage Download PDF

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Publication number
WO2022089190A1
WO2022089190A1 PCT/CN2021/123176 CN2021123176W WO2022089190A1 WO 2022089190 A1 WO2022089190 A1 WO 2022089190A1 CN 2021123176 W CN2021123176 W CN 2021123176W WO 2022089190 A1 WO2022089190 A1 WO 2022089190A1
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user
recommended
product
historical data
grouping
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PCT/CN2021/123176
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English (en)
Chinese (zh)
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张�杰
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深圳壹账通智能科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance

Definitions

  • the present application relates to the field of artificial intelligence technology and data analysis, and in particular, to a product recommendation method, device, electronic device, and readable storage medium.
  • the product recommendations provided in this application include:
  • Parse the product recommendation request sent by the user based on the client obtain the identifier of the user to be recommended carried in the product recommendation request, acquire the second historical data of the user to be recommended from the database based on the identifier, and determine the The target user group corresponding to the user to be recommended;
  • a target product is recommended for the user to be recommended based on the product penetration rate array corresponding to the target user group.
  • the present application also provides a product recommendation device, the device comprising:
  • the grouping module is used for obtaining the first historical data of each user in the database, determining the first characteristic of each user based on the first characteristic factor and the first historical data, and grouping each user based on the first characteristic to obtain multiple user groups;
  • a calculation module configured to calculate the product penetration rate array corresponding to each user group in the multiple user groups based on the first historical data
  • the parsing module is configured to parse the product recommendation request sent by the user based on the client, obtain the identifier of the user to be recommended carried in the product recommendation request, and obtain the second historical data of the user to be recommended from the database based on the identifier , and determine the target user group corresponding to the user to be recommended;
  • a determination module configured to obtain the second characteristic factor corresponding to the product recommendation request, determine the second characteristic of the user to be recommended based on the second characteristic factor and the second historical data, and input the second characteristic into The attribute analysis model obtains the target attribute value of the user to be recommended;
  • a recommendation module configured to recommend a target product for the user to be recommended based on the product penetration rate array corresponding to the target user group when the target attribute value is less than a preset threshold.
  • the present application also provides an electronic device, the electronic device comprising:
  • the memory stores a product recommendation program executable by the at least one processor, and the product recommendation program is executed by the at least one processor to enable the at least one processor to perform the following steps:
  • Parse the product recommendation request sent by the user based on the client obtain the identifier of the user to be recommended carried in the product recommendation request, acquire the second historical data of the user to be recommended from the database based on the identifier, and determine the The target user group corresponding to the user to be recommended;
  • a target product is recommended for the user to be recommended based on the product penetration rate array corresponding to the target user group.
  • the present application also provides a computer-readable storage medium, where a product recommendation program is stored on the computer-readable storage medium, and the product recommendation program can be executed by one or more processors to implement the following steps:
  • Parse the product recommendation request sent by the user based on the client obtain the identifier of the user to be recommended carried in the product recommendation request, acquire the second historical data of the user to be recommended from the database based on the identifier, and determine the The target user group corresponding to the user to be recommended;
  • a target product is recommended for the user to be recommended based on the product penetration rate array corresponding to the target user group.
  • FIG. 1 is a schematic flowchart of a product recommendation method provided by an embodiment of the present application.
  • FIG. 2 is a schematic diagram of a module of a product recommendation device provided by an embodiment of the present application
  • FIG. 3 is a schematic structural diagram of an electronic device for implementing a product recommendation method provided by an embodiment of the present application
  • the embodiments of the present application may acquire and process related data based on artificial intelligence technology.
  • Artificial Intelligence is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .
  • the basic technologies of artificial intelligence generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, and mechatronics.
  • Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning/deep learning.
  • the present application provides a product recommendation method.
  • FIG. 1 it is a schematic flowchart of a product recommendation method according to an embodiment of the present application.
  • the method may be performed by an electronic device, which may be implemented by software and/or hardware.
  • the product recommendation method includes:
  • the first historical data includes the basic information and asset information of the user
  • the first characteristic factor includes the user's gender, age, educational background, age of accounts, holdings in the basic information.
  • the first feature is obtained by splicing the feature values corresponding to each feature in the basic information and the asset information.
  • the grouping of each user based on the first feature to obtain a plurality of user groups includes:
  • K-means clustering algorithm uses the K-means clustering algorithm to group each user based on the first feature, where K respectively takes a value of each natural number within a preset value range, and a value of K corresponds to a grouping result, and obtains multiple grouping results;
  • K represents the number of user groups.
  • K is any natural number from 3 to 10, then it can be divided into 3 user groups, 4 user groups, ..., 9 user groups, Divided into 10 user groups with a total of 8 grouping results.
  • A2 determine the center user of each user group corresponding to each kind of grouping result in the multiple grouping results, and calculate the corresponding silhouette coefficient of each kind of grouping result based on the first feature of the center user;
  • the existing calculation method of the silhouette coefficient has high complexity, wherein the calculation formula of the silhouette coefficient corresponding to the user is:
  • S ij represents the silhouette coefficient corresponding to the j th user in the ith grouping result, represents the average distance from the first feature of the jth user in the i-th grouping result to the first features of other users in the same user group, Indicates the minimum value of the average distance between the first feature of the jth user and the first features of other user groups in the ith grouping result.
  • S i represents the contour coefficient corresponding to the i-th grouping result
  • S ij represents the contour coefficient corresponding to the j-th user in the i-th grouping result
  • n represents the total number of users.
  • the silhouette coefficient is an evaluation method for the quality of the grouping results, which reflects the cohesion and separation of the clustering method. If the inner clustering of the same cluster is higher, and the separation degree of different clusters is higher, the clustering effect is better, and the closer S ij is to 1, it means The smaller the value, the better the clustering effect.
  • the present application proposes a simplified method for calculating the silhouette coefficient.
  • the centroid of each cluster is known due to the K-means algorithm.
  • the centroid is the core point of each cluster, and other points in the cluster are close to it, so the centroid can approximately represent the entire cluster.
  • the determining of the central user (ie the centroid) of each user group corresponding to each of the multiple grouping results includes:
  • the central user of each user group may also be determined in other manners, and the present application does not limit the determination manner of the central user.
  • S pq represents the silhouette coefficient corresponding to the qth user group in the pth grouping result, represents the average distance from the first feature of the central user of the qth user group in the pth grouping result to the first features of other users in the same user group, Represents the minimum value of the average distance between the first feature of the central user of the qth user group and the first features of other user groups in the pth grouping result, Sp represents the contour coefficient corresponding to the pth grouping result, m represents the th The total number of user groups in p types of grouping results.
  • the present application simplifies the complexity of calculating the silhouette coefficient from n 2 to mn, where n is the total number of users and m is the number of user groups. Therefore, the present application greatly improves the grouping efficiency.
  • the grouping result with the contour coefficient closest to the preset value is used as the target grouping result.
  • the preset value is 1, and the grouping result with the contour coefficient closest to 1 is used as the target grouping result.
  • the calculating, based on the first historical data, an array of product penetration rates corresponding to each of the multiple user groups includes:
  • the first historical data also includes the user's product purchase information
  • the product penetration rate in the product penetration rate array corresponding to a certain user group is the number of people who purchased each product in the user group and the total users of the user group percentage of the quantity.
  • the product penetration rate array corresponding to user group 1 is ⁇ 20%, 5%, 50%, 2%, 6%, ..., 2% ⁇ . According to the product penetration rate array, different user groups can be analyzed. The degree of preference for different products can be used for product recommendation.
  • the historical data of the user to be recommended can be obtained from the database, it means that the user to be recommended is an existing user, and the target user group information corresponding to the user to be recommended can be obtained based on the user ID.
  • the absolute value of the difference between the recommended user and the first characteristic of the central user of each user group, and the user group to which the central user corresponding to the smallest absolute value of the difference belongs is used as the target user group of the user to be recommended.
  • the method further includes:
  • the user to be recommended is a new user and no data information has been generated. At this time, the preconfigured product list for the new user is recommended to the new user.
  • the attribute analysis model is used to analyze the loss probability of users, and the attribute analysis model is a random forest model.
  • the second feature factor includes volatility feature, trend feature, product hobby feature, and activity feature.
  • time slices are used to construct the second feature of the user, for example, the first 6 months of data are selected from the second historical data. The data and the data of the previous 3 months are used to determine the eigenvalues corresponding to each eigenfactor of the user.
  • the characteristic values corresponding to the volatility characteristics include: the asset range in the past three months (the difference between the maximum asset value and the asset minimum value), the asset standard deviation in the past three months, the asset range in the past six months, and the asset range in the past six months.
  • the standard deviation of assets in 6 months; the characteristic values corresponding to trend characteristics include: asset growth rate in the past 3 months, growth rate of the number of individual gold products held in the past 3 months, asset growth rate in the past 6 months, and recent asset growth rate.
  • the feature values corresponding to the feature include: ageing, average number of transactions in the past 3 months, average number of transactions in the past 6 months, etc.
  • the second feature is obtained by splicing the feature values corresponding to the aforementioned volatility feature, trend feature, product hobby feature, and activity feature, and the second feature is input into the attribute analysis model to obtain the target attribute value of the user to be recommended (ie, loss probability) .
  • the target attribute value that is, the churn probability
  • the preset threshold it means that the probability of user churn is low.
  • the product penetration rate array corresponding to the target user group to which the user to be recommended belongs is obtained, and the products with the highest product penetration rate are used as The target product is recommended to the user to be recommended.
  • the method further includes:
  • the target attribute value is greater than the preset threshold, calculate the index values of multiple preset indexes based on the second historical data, and when the index value corresponding to a specified index among the multiple preset indexes is greater than the index threshold , recommending a target product for the user to be recommended based on the specified index.
  • the preset index is a preset index that has a greater impact on the churn rate, and the user churn can be determined based on the preset index Reasons and recommend suitable products.
  • the standard deviation of assets is a preset indicator. When the standard deviation of assets is greater than the indicator threshold, it means that the assets of the users to be recommended have changed greatly. In order to reduce the standard deviation of assets, you can recommend large-denomination certificates of deposit such as Long-term financial products are recommended to users to be recommended.
  • each user is grouped according to the first feature to obtain a plurality of user groups, and the product penetration rate array corresponding to each user group is calculated based on the first historical data.
  • the degree of preference of different user groups for different products is analyzed, which can be helpful for accurate recommendation; then, the second historical data of the user to be recommended is obtained, and the target user group corresponding to the user to be recommended is determined.
  • the historical data determines the second feature of the user to be recommended, and the second feature is input into the attribute analysis model to obtain the target attribute value (ie, the churn rate) of the user to be recommended.
  • the product corresponding to the target user group The penetration rate array is the target product recommended by the user to be recommended.
  • product recommendation is performed based on the product penetration rate array when the user churn rate is low, so that the success rate of product recommendation is higher. Therefore, the present application improves the success rate of product recommendation.
  • FIG. 2 it is a schematic diagram of a module of a product recommendation apparatus provided by an embodiment of the present application.
  • the product recommendation apparatus 100 described in this application may be installed in an electronic device. According to the implemented functions, the product recommendation apparatus 100 may include a grouping module 110 , a calculation module 120 , a parsing module 130 , a determination module 140 and a recommendation module 150 .
  • the modules described in this application may also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can perform fixed functions, and are stored in the memory of the electronic device.
  • each module/unit is as follows:
  • the grouping module 110 is configured to obtain the first historical data of each user in the database, determine the first characteristic of each user based on the first characteristic factor and the first historical data, and group each user based on the first characteristic to obtain Multiple user groups.
  • the first historical data includes the basic information and asset information of the user
  • the first characteristic factor includes the user's gender, age, educational background, age of accounts, holdings in the basic information.
  • the first feature is obtained by splicing the feature values corresponding to each feature in the basic information and the asset information.
  • the grouping of each user based on the first feature to obtain a plurality of user groups includes:
  • K-means clustering algorithm uses the K-means clustering algorithm to group each user based on the first feature, where K respectively takes a value of each natural number within a preset value range, and a value of K corresponds to a grouping result, and obtains multiple grouping results;
  • K represents the number of user groups.
  • K is any natural number from 3 to 10, then it can be divided into 3 user groups, 4 user groups, ..., 9 user groups, Divided into 10 user groups with a total of 8 grouping results.
  • A2 determine the center user of each user group corresponding to each kind of grouping result in the multiple grouping results, and calculate the corresponding silhouette coefficient of each kind of grouping result based on the first feature of the center user;
  • the existing calculation method of the silhouette coefficient has high complexity, wherein the calculation formula of the silhouette coefficient corresponding to the user is:
  • S ij represents the silhouette coefficient corresponding to the j th user in the ith grouping result, represents the average distance from the first feature of the jth user in the i-th grouping result to the first features of other users in the same user group, Indicates the minimum value of the average distance between the first feature of the jth user and the first features of other user groups in the ith grouping result.
  • S i represents the contour coefficient corresponding to the i-th grouping result
  • S ij represents the contour coefficient corresponding to the j-th user in the i-th grouping result
  • n represents the total number of users.
  • the silhouette coefficient is an evaluation method for the quality of the grouping results, which reflects the cohesion and separation of the clustering method. If the inner clustering of the same cluster is higher, and the separation degree of different clusters is higher, the clustering effect is better, and the closer S ij is to 1, it means The smaller the value, the better the clustering effect.
  • the present application proposes a simplified method for calculating the silhouette coefficient.
  • the centroid of each cluster is known due to the K-means algorithm.
  • the centroid is the core point of each cluster, and other points in the cluster are close to it, so the centroid can approximately represent the entire cluster.
  • the determining of the central user (ie the centroid) of each user group corresponding to each of the multiple grouping results includes:
  • the central user of each user group may also be determined in other manners, and the present application does not limit the determination manner of the central user.
  • S pq represents the silhouette coefficient corresponding to the qth user group in the pth grouping result, represents the average distance from the first feature of the central user of the qth user group in the pth grouping result to the first features of other users in the same user group, Represents the minimum value of the average distance between the first feature of the central user of the qth user group and the first features of other user groups in the pth grouping result, Sp represents the contour coefficient corresponding to the pth grouping result, m represents the th The total number of user groups in p types of grouping results.
  • the present application simplifies the complexity of calculating the silhouette coefficient from n 2 to mn, where n is the total number of users and m is the number of user groups. Therefore, the present application greatly improves the grouping efficiency.
  • the grouping result with the contour coefficient closest to the preset value is used as the target grouping result.
  • the preset value is 1, and the grouping result with the contour coefficient closest to 1 is used as the target grouping result.
  • the calculation module 120 is configured to calculate a product penetration rate array corresponding to each user group in the plurality of user groups based on the first historical data.
  • the calculating, based on the first historical data, an array of product penetration rates corresponding to each of the multiple user groups includes:
  • the first historical data also includes the user's product purchase information
  • the product penetration rate in the product penetration rate array corresponding to a certain user group is the number of people who purchased each product in the user group and the total users of the user group percentage of the quantity.
  • the product penetration rate array corresponding to user group 1 is ⁇ 20%, 5%, 50%, 2%, 6%, ..., 2% ⁇ . According to the product penetration rate array, different user groups can be analyzed. The degree of preference for different products can be used for product recommendation.
  • the parsing module 130 is configured to parse the product recommendation request sent by the user based on the client, obtain the identifier of the user to be recommended carried in the product recommendation request, and obtain the second history of the user to be recommended from the database based on the identifier data, and determine the target user group corresponding to the user to be recommended;
  • the historical data of the user to be recommended can be obtained from the database, it means that the user to be recommended is an existing user, and the target user group information corresponding to the user to be recommended can be obtained based on the user ID.
  • the absolute value of the difference between the recommended user and the first characteristic of the central user of each user group, and the user group to which the central user corresponding to the smallest absolute value of the difference belongs is used as the target user group of the user to be recommended.
  • the parsing module 130 is further configured to:
  • the user to be recommended is a new user and no data information has been generated. At this time, the preconfigured product list for the new user is recommended to the new user.
  • the determining module 140 is configured to obtain the second characteristic factor corresponding to the product recommendation request, determine the second characteristic of the user to be recommended based on the second characteristic factor and the second historical data, and assign the second characteristic
  • the attribute analysis model is input to obtain the target attribute value of the user to be recommended.
  • the attribute analysis model is used to analyze the loss probability of users, and the attribute analysis model is a random forest model.
  • the second feature factor includes volatility feature, trend feature, product hobby feature, and activity feature.
  • time slices are used to construct the second feature of the user, for example, the first 6 months of data are selected from the second historical data. The data and the data of the previous 3 months are used to determine the eigenvalues corresponding to each eigenfactor of the user.
  • the characteristic values corresponding to the volatility characteristics include: the asset range in the past three months (the difference between the maximum asset value and the asset minimum value), the asset standard deviation in the past three months, the asset range in the past six months, and the asset range in the past six months.
  • the standard deviation of assets in 6 months; the characteristic values corresponding to trend characteristics include: asset growth rate in the past 3 months, growth rate of the number of individual gold products held in the past 3 months, asset growth rate in the past 6 months, and recent asset growth rate.
  • the feature values corresponding to the feature include: ageing, average number of transactions in the past 3 months, average number of transactions in the past 6 months, etc.
  • the second feature is obtained by splicing the feature values corresponding to the aforementioned volatility feature, trend feature, product hobby feature, and activity feature, and the second feature is input into the attribute analysis model to obtain the target attribute value of the user to be recommended (ie, loss probability) .
  • the recommendation module 150 is configured to recommend a target product for the user to be recommended based on the product penetration rate array corresponding to the target user group when the target attribute value is less than a preset threshold.
  • the target attribute value that is, the churn probability
  • the preset threshold it means that the probability of user churn is low.
  • the product penetration rate array corresponding to the target user group to which the user to be recommended belongs is obtained, and the products with the highest product penetration rate are used as The target product is recommended to the user to be recommended.
  • the recommendation module 150 is further configured to:
  • the target attribute value is greater than the preset threshold, calculate the index values of multiple preset indexes based on the second historical data, and when the index value corresponding to a specified index among the multiple preset indexes is greater than the index threshold , recommending a target product for the user to be recommended based on the specified index.
  • the preset index is a preset index that has a greater impact on the churn rate, and the user churn can be determined based on the preset index Reasons and recommend suitable products.
  • the standard deviation of assets is a preset indicator. When the standard deviation of assets is greater than the indicator threshold, it means that the assets of the users to be recommended have changed greatly. In order to reduce the standard deviation of assets, you can recommend large-denomination certificates of deposit such as Long-term financial products are recommended to users to be recommended.
  • FIG. 3 it is a schematic structural diagram of an electronic device for implementing a product recommendation method according to an embodiment of the present application.
  • the electronic device 1 is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions.
  • the electronic device 1 may be a computer, a single network server, a server group composed of multiple network servers, or a cloud based on cloud computing composed of a large number of hosts or network servers, wherein cloud computing is a kind of distributed computing, A super virtual computer consisting of a collection of loosely coupled computers.
  • the electronic device 1 includes, but is not limited to, a memory 11 , a processor 12 , and a network interface 13 that can be communicatively connected to each other through a system bus, the memory 11 stores a product recommendation program 10 , and the product recommendation program 10 is executable by the processor 12 .
  • FIG. 3 only shows the electronic device 1 having the components 11-13 and the product recommendation program 10. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device 1, and may include a Fewer or more components are shown, or some components are combined, or a different arrangement of components.
  • the memory 11 includes a memory and at least one type of readable storage medium.
  • the memory provides a cache for the operation of the electronic device 1;
  • the readable storage medium can be, for example, flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM) ), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. non-volatile storage media.
  • the readable storage medium may also be a volatile storage medium.
  • the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1; in other embodiments, the storage medium may also be an external storage device of the electronic device 1, such as A pluggable hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash memory card (Flash Card), etc., are equipped on the electronic device 1.
  • the readable storage medium of the memory 11 is generally used to store the operating system and various application software installed in the electronic device 1 , for example, to store the code of the product recommendation program 10 in an embodiment of the present application.
  • the memory 11 can also be used to temporarily store various types of data that have been output or will be output.
  • the processor 12 may be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chips.
  • the processor 12 is generally used to control the overall operation of the electronic device 1, such as performing control and processing related to data interaction or communication with other devices.
  • the processor 12 is configured to run the program code or process data stored in the memory 11, for example, run the product recommendation program 10 and the like.
  • the network interface 13 may include a wireless network interface or a wired network interface, and the network interface 13 is used to establish a communication connection between the electronic device 1 and a client (not shown in the figure).
  • the electronic device 1 may further include a user interface, and the user interface may include a display (Display), an input unit such as a keyboard (Keyboard), and an optional user interface may also include a standard wired interface and a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like.
  • the display may also be appropriately called a display screen or a display unit, which is used for displaying information processed in the electronic device 1 and for displaying a visualized user interface.
  • the product recommendation program 10 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, and when running in the processor 12, can realize:
  • Parse the product recommendation request sent by the user based on the client obtain the identifier of the user to be recommended carried in the product recommendation request, acquire the second historical data of the user to be recommended from the database based on the identifier, and determine the The target user group corresponding to the user to be recommended;
  • a target product is recommended for the user to be recommended based on the product penetration rate array corresponding to the target user group.
  • the above-mentioned product recommendation program 10 by the processor 12, reference may be made to the description of the relevant steps in the corresponding embodiment of FIG. 1 , which will not be repeated here. It should be emphasized that, in order to further ensure the privacy and security of the above-mentioned first and second historical data, the above-mentioned first and second historical data may also be stored in a node of a blockchain.
  • the modules/units integrated in the electronic device 1 may be stored in a computer-readable storage medium.
  • the computer-readable storage medium may be non-volatile or non-volatile.
  • the computer-readable storage medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory) ).
  • a product recommendation program 10 is stored on the computer-readable storage medium, and the product recommendation program 10 can be executed by one or more processors to realize the following steps:
  • Parse the product recommendation request sent by the user based on the client obtain the identifier of the user to be recommended carried in the product recommendation request, acquire the second historical data of the user to be recommended from the database based on the identifier, and determine the The target user group corresponding to the user to be recommended;
  • a target product is recommended for the user to be recommended based on the product penetration rate array corresponding to the target user group.
  • modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.
  • the blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

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Abstract

La présente demande se rapporte au domaine technique de l'intelligence artificielle. La demande concerne un procédé de recommandation de produit consistant : à déterminer des premières caractéristiques d'utilisateurs et à regrouper les utilisateurs sur la base des premières caractéristiques de sorte à obtenir une pluralité de groupes d'utilisateurs ; à calculer un réseau de perméabilité de produit correspondant à chaque groupe d'utilisateurs dans la pluralité de groupes d'utilisateurs ; à déterminer un groupe d'utilisateurs cible correspondant à un utilisateur à qui il faut donner une recommandation ; à acquérir un second facteur de caractéristique correspondant à une demande de recommandation de produit, à déterminer, sur la base du second facteur de caractéristique et des secondes données historiques, une seconde caractéristique de l'utilisateur à qui il faut donner une recommandation, et à entrer la seconde caractéristique dans un modèle d'analyse d'attribut de sorte à obtenir une valeur d'attribut cible de l'utilisateur à qui il faut donner une recommandation ; et lorsque la valeur d'attribut cible est inférieure à une valeur de seuil prédéfinie, sur la base du réseau de perméabilité de produit correspondant au groupe d'utilisateurs cible, à recommander un produit cible à l'utilisateur à qui il faut donner une recommandation. La demande concerne en outre un appareil de recommandation de produit, un dispositif électronique et un support de stockage lisible. Au moyen de la présente demande, le taux de réussite d'une recommandation de produit est améliorée.
PCT/CN2021/123176 2020-11-02 2021-10-12 Procédé et appareil de recommandation de produit, ainsi que dispositif électronique et support de stockage WO2022089190A1 (fr)

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