WO2019085120A1 - Procédé de recommandation à filtrage collaboratif, dispositif électronique, et support d'informations lisible par ordinateur - Google Patents

Procédé de recommandation à filtrage collaboratif, dispositif électronique, et support d'informations lisible par ordinateur Download PDF

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WO2019085120A1
WO2019085120A1 PCT/CN2017/113724 CN2017113724W WO2019085120A1 WO 2019085120 A1 WO2019085120 A1 WO 2019085120A1 CN 2017113724 W CN2017113724 W CN 2017113724W WO 2019085120 A1 WO2019085120 A1 WO 2019085120A1
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tag
user
target user
correlation
label
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PCT/CN2017/113724
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English (en)
Chinese (zh)
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李芳�
王建明
肖京
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平安科技（深圳）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

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  • the present application relates to the field of computer information technology, and in particular, to a collaborative filtering recommendation method, an electronic device, and a computer readable storage medium.
  • the traditional collaborative filtering recommendation methods mainly include user-based collaborative filtering recommendation and project-based collaborative filtering recommendation.
  • the traditional collaborative filtering recommendation algorithm may not be able to perform similarity calculation.
  • the design of the collaborative filtering recommendation method in the prior art is not reasonable enough and needs to be improved.
  • the present application proposes a collaborative filtering recommendation method, an electronic device, and a computer readable storage medium, and solves the sparseness and interest of the traditional collaborative filtering method scoring matrix by introducing a user-tag correlation matrix and a tag-item correlation matrix.
  • the single problem of the model reduces the scale of the scoring matrix and improves the efficiency of the algorithm.
  • the present application provides an electronic device, where the electronic device includes a memory and a processor, where the memory stores a collaborative filtering recommendation system that can be run on the processor, and the collaborative filtering recommendation
  • the system implements the following steps when executed by the processor:
  • the user-tag correlation matrix adopts a two-dimensional matrix, each row of the two-dimensional matrix represents a user, and each column represents a label, and the two-dimensional matrix stores a correlation between all users and all tags;
  • the similarity between the target user and different designated users is calculated by using a first calculation formula.
  • the first calculation formula is set to Equation 1:
  • Equation 1 Sa a, s represents the similarity between the target user u a and the specified user u s , and T a, s represents the label used by the target user u a and the specified user u s , and r a, t represents the target
  • the correlation between the user u a and the tag t, r s,t represents the correlation between the specified user u s and the tag t,
  • the average of the correlation between the user u a and all tags Represents the average of the correlation of user u s with all tags.
  • the correlation between the target user and each candidate tag is calculated by using a second calculation formula, and the second calculation formula is set to formula 2:
  • Equation 2 P a,k represents the correlation of the target user u a with the candidate tag t k , U K represents the user set using the tag t k , U N represents all users of the nearest neighbor set, Sa , u Representing the similarity between the target user u a and the specified user u u , r u, represents the correlation between the specified user u u and the candidate tag t k .
  • the recommending the item related to the original label and the newly added label to the target user comprises:
  • the correlation between the selection label and each item in the project set is calculated by using a third calculation formula, and the third calculation formula is set to formula 3:
  • Equation 3 t 1 represents an original tag of the target user and a tag in the newly added tag set T N , i represents an item in the item set I t1 marked by the tag t 1 , and relat(t 1 , i) represents The correlation of the tag t 1 with the item i, countUser(t 1 , i) represents the number of users associated with item i and tag t 1 , and countUser(t 1 , j) represents the number of users associated with item j and tag t 1 .
  • the present application further provides a collaborative filtering recommendation method, which is applied to an electronic device, and the method includes:
  • the user-tag correlation matrix adopts a two-dimensional matrix, each row of the two-dimensional matrix represents a user, and each column represents a label, and the two-dimensional matrix stores a correlation between all users and all tags;
  • the similarity between the target user and different specified users is calculated by using a first calculation formula, and the first calculation formula is set to formula 1:
  • Equation 1 Sa a, s represents the similarity between the target user u a and the specified user u s , and T a, s represents the label used by the target user u a and the specified user u s , and r a, t represents the target
  • the correlation between the user u a and the tag t, r s,t represents the correlation between the specified user u s and the tag t,
  • the average of the correlation between the user u a and all tags Represents the average of the correlation of user u s with all tags.
  • the correlation between the target user and each candidate tag is calculated by using a second calculation formula, and the second calculation formula is set to formula 2:
  • Equation 2 P a,k represents the correlation of the target user u a with the candidate tag t k , U K represents the user set using the tag t k , U N represents all users of the nearest neighbor set, Sa , u Representing the similarity between the target user u a and the specified user u u , r u, represents the correlation between the specified user u u and the candidate tag t k .
  • the recommending the item related to the original label and the newly added label to the target user comprises:
  • the correlation between the selection label and each item in the project set is calculated by using a third calculation formula, and the third calculation formula is set to formula 3:
  • Equation 3 t 1 represents an original tag of the target user and a tag in the newly added tag set T N , i represents an item in the item set I t1 marked by the tag t 1 , and relat(t 1 , i) represents The correlation of the tag t 1 with the item i, countUser(t 1 , i) represents the number of users associated with item i and tag t 1 , and countUser(t 1 , j) represents the number of users associated with item j and tag t 1 .
  • the present application further provides a computer readable storage medium,
  • the computer readable storage medium stores a collaborative filtering recommendation system executable by at least one processor to cause the at least one processor to perform the steps of the collaborative filtering recommendation method as described above.
  • the electronic device, the collaborative filtering recommendation method and the computer readable storage medium proposed by the present application solve the traditional collaborative filtering method scoring matrix by introducing a user-tag correlation matrix and a tag-item correlation matrix.
  • the sparseness and interest model are single, which reduces the scale of the scoring matrix, improves the efficiency of the algorithm, and enhances the scalability of the algorithm.
  • the user label-based collaborative filtering recommendation effect used in this application is superior to the traditional collaborative filtering method.
  • 1 is a schematic diagram of an optional hardware architecture of an electronic device of the present application
  • FIG. 2 is a schematic diagram of a program module of an embodiment of a collaborative filtering recommendation system in an electronic device of the present application
  • FIG. 3 is a schematic diagram of an implementation process of an embodiment of a collaborative filtering recommendation method according to the present application.
  • FIG. 1 it is a schematic diagram of an optional hardware architecture of the electronic device 2 of the present application.
  • the electronic device 2 may include, but is not limited to, a memory 21, a processor 22, and a network interface 23 that can communicate with each other through a system bus. It is pointed out that FIG. 1 only shows the electronic device 2 with the components 21-23, but it should be understood that not all illustrated components are required to be implemented, and more or fewer components may be implemented instead.
  • the electronic device 2 may be a computing device such as a rack server, a blade server, a tower server, or a rack server.
  • the electronic device 2 may be an independent server or a server cluster composed of multiple servers. .
  • the memory 21 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a 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, and the like.
  • the memory 21 may be an internal storage unit of the electronic device 2, such as a hard disk or memory of the electronic device 2.
  • the memory 21 may also be an external storage device of the electronic device 2, such as a plug-in hard disk equipped on the electronic device 2, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc.
  • the memory 21 may also include both an internal storage unit of the electronic device 2 and an external storage device thereof.
  • the memory 21 is generally used to store an operating system installed in the electronic device 2 and various types of application software, such as program code of the collaborative filtering recommendation system 20. Further, the memory 21 can also be used to temporarily store various types of data that have been output or are to be output.
  • the processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. This treatment The device 22 is typically used to control the overall operation of the electronic device 2, such as performing control and processing associated with data interaction or communication with the electronic device 2. In this embodiment, the processor 22 is configured to run program code or process data stored in the memory 21, such as running the collaborative filtering recommendation system 20 and the like.
  • CPU Central Processing Unit
  • controller microcontroller
  • microprocessor microprocessor
  • the network interface 23 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the electronic device 2 and other electronic devices.
  • the network interface 23 is configured to connect the electronic device 2 to an external data platform through a network, and establish a data transmission channel and a communication connection between the electronic device 2 and an external data platform.
  • the network may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, or a 5G network.
  • Wireless or wired networks such as network, Bluetooth, Wi-Fi, etc.
  • the collaborative filtering recommendation system 20 may be divided into one or more program modules, the one or more program modules being stored in the memory 21 and being processed by one or more processors ( This embodiment is executed by the processor 22) to complete the application.
  • the collaborative filtering recommendation system 20 can be divided into a computing module 201, a selection module 202, and a recommendation module 203.
  • the program module referred to in the present application refers to a series of computer program instruction segments capable of performing a specific function, and is more suitable than the program to describe the execution process of the collaborative filtering recommendation system 20 in the electronic device 2. The function of each program module 201-203 will be described in detail below.
  • the calculating module 201 is configured to calculate a similarity between the target user and different specified users according to the user-tag correlation matrix, and select the first predetermined number of designated users according to the order of similarity from high to low (if the similarity is high)
  • the first 10 specified users are the closest neighbor collection of the target user.
  • the user-tag correlation matrix adopts a two-dimensional matrix, each row of the two-dimensional matrix represents a user, and each column represents a tag (such as a property insurance user tag, etc.). Further, the two-dimensional matrix stores the correlation between all users and all tags.
  • the similarity between the target user and different specified users is calculated by using a first calculation formula, wherein the first calculation formula may be set as shown in the following formula 1.
  • the Sa , s represents a similarity between the target user u a and the specified user u s
  • T a s represents a label used by the target user u a and the specified user u s
  • r a, t represents the target
  • the correlation between the user u a and the tag t, r s,t represents the correlation between the specified user u s and the tag t
  • the average of the correlation between the user u a and all tags Represents the average of the correlation of user u s with all tags.
  • the selecting module 202 is configured to select, from the user tags of the nearest neighbor set, a tag that is not used by the target user, as a candidate tag of the target user (recorded as a set).
  • the calculation module 201 is further configured to calculate a correlation between the target user and each candidate tag, and select a second predetermined number of candidate tags according to a high-to-low correlation (for example, the first three related correlations) Candidate tag) as a new tag for this target user.
  • a high-to-low correlation for example, the first three related correlations
  • the correlation between the target user and each candidate tag is calculated by using a second calculation formula, wherein the second calculation formula may be set as shown in the following formula 2.
  • the P a,k represents the correlation between the target user u a and the candidate tag t k
  • U K represents the user set using the tag t k
  • Sa a specified number of users
  • u represents the similarity between the target user u a and the specified user u u
  • r u, k represents the relationship between the specified user u u and the candidate tag t k Correlation (ie, assigning the user u u to use the weight of the candidate tag t k ).
  • the recommendation module 203 is configured to recommend an item related to the original label and the newly added label to the target user according to the original label and the newly added label of the target user.
  • the original label of the target user may be a label of the target user originally stored in the user-tag correlation matrix.
  • the recommending the item related to the original label and the newly added label to the target user includes the following steps:
  • the third predetermined number of items are selected and recommended to the target user.
  • the correlation between the selection label and each item in the item set is calculated by using a third calculation formula, wherein the third calculation formula may be set as shown in the following formula 3.
  • t 1 represents an original label of the target user and a label in the newly added label set T N (ie, t 1 ⁇ T N )
  • i represents an item in the item set I t1 marked by the label t 1 , t 1 , i) represents the correlation of the tag t 1 with the item i
  • countUser(t 1 , i) represents the number of users associated with the item i and the tag t 1
  • countUser(t 1 , j) represents the item j and the tag t 1
  • the number of related users, the right denominator part of Equation 3 represents the number of all users associated with the tag t 1 in the item set I t1 .
  • the collaborative filtering recommendation system 20 proposed by the present application solves the problem of sparse scoring matrix and single interest model of the traditional collaborative filtering method by introducing a user-tag correlation matrix and a tag-item correlation matrix.
  • the scale of the scoring matrix is reduced, the efficiency of the algorithm is improved, and the scalability of the algorithm is enhanced.
  • the collaborative filtering recommendation effect based on user tags used in the present application is superior to the traditional collaborative filtering method.
  • the present application also proposes a collaborative filtering recommendation method.
  • FIG. 3 it is a schematic flowchart of an implementation process of an embodiment of the collaborative filtering recommendation method of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 3 may be changed according to different requirements, and some steps may be omitted.
  • Step S31 calculating the similarity between the target user and different designated users according to the user-tag correlation matrix, and selecting the first predetermined number of designated users according to the order of similarity from high to low (eg, the top 10 designations with higher similarity) User) as the closest neighbor set for the target user.
  • the user-tag correlation matrix adopts a two-dimensional matrix, each row of the two-dimensional matrix represents a user, and each column represents a tag (such as a property insurance user tag, etc.). Further, the two-dimensional matrix stores the correlation between all users and all tags.
  • the similarity between the target user and different specified users is calculated by using a first calculation formula, wherein the first calculation formula may be set as shown in the following formula 1.
  • the Sa , s represents a similarity between the target user u a and the specified user u s
  • T a s represents a label used by the target user u a and the specified user u s
  • r a, t represents the target
  • the correlation between the user u a and the tag t, r s,t represents the correlation between the specified user u s and the tag t
  • the average of the correlation between the user u a and all tags Represents the average of the correlation of user u s with all tags.
  • Step S32 Select a label that is not used by the target user from the user labels of the nearest neighbor set as a candidate label of the target user (recorded as a set).
  • Step S33 calculating a correlation between the target user and each candidate tag, and selecting a second predetermined number of candidate tags (such as the top 3 candidate tags with higher correlation) as the target according to the order of relevance from high to low. User's new label.
  • the correlation between the target user and each candidate tag is calculated by using a second calculation formula, wherein the second calculation formula may be set as shown in the following formula 2.
  • the P a,k represents the correlation between the target user u a and the candidate tag t k
  • U K represents the user set using the tag t k
  • Sa a specified number of users
  • u represents the similarity between the target user u a and the specified user u u
  • r u, k represents the relationship between the specified user u u and the candidate tag t k Correlation (ie, assigning the user u u to use the weight of the candidate tag t k ).
  • Step S34 recommending items related to the original label and the newly added label to the target user according to the original label and the newly added label of the target user.
  • the original label of the target user may be a label of the target user originally stored in the user-tag correlation matrix.
  • the recommending the item related to the original label and the newly added label to the target user includes the following steps:
  • the third predetermined number of items are selected and recommended to the target user.
  • the correlation between the selection label and each item in the item set is calculated by using a third calculation formula, wherein the third calculation formula may be set as shown in the following formula 3.
  • t 1 represents an original label of the target user and a label in the newly added label set T N (ie, t 1 ⁇ T N )
  • i represents an item in the item set I t1 marked by the label t 1 , t 1 , i) represents the correlation of the tag t 1 with the item i
  • countUser(t 1 , i) represents the number of users associated with the item i and the tag t 1
  • countUser(t 1 , j) represents the item j and the tag t 1
  • the number of related users, the right denominator part of Equation 3 represents the number of all users associated with the tag t 1 in the item set I t1 .
  • the collaborative filtering recommendation method proposed by the present application solves the problem of sparse scoring matrix and single interest model of the traditional collaborative filtering method by introducing a user-tag correlation matrix and a label-item correlation matrix, and reduces the problem.
  • the scale of the scoring matrix improves the efficiency of the algorithm and enhances the scalability of the algorithm.
  • the user label-based collaborative filtering recommendation effect used in this application is superior to the traditional collaborative filtering method.
  • the present application further provides a computer readable storage medium (such as a ROM/RAM, a magnetic disk, an optical disk), where the computer readable storage medium stores a collaborative filtering recommendation system 20, and the collaborative filtering
  • the recommendation system 20 can be executed by at least one processor 22 to cause the at least one processor 22 to perform the steps of the collaborative filtering recommendation method as described above.
  • the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and can also be implemented by hardware, but in many cases, the former is A better implementation.
  • the technical solution of the present application which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk,
  • the optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

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Abstract

La présente invention concerne un procédé de recommandation à filtrage collaboratif. Le procédé comprend les étapes consistant à : calculer des similarités d'un utilisateur cible avec différents utilisateurs désignés, selon une matrice de corrélation d'étiquettes d'utilisateur, et sélectionner un premier nombre prédéfini d'utilisateurs désignés en tant qu'ensemble voisin le plus proche de l'utilisateur cible selon un ordre décroissant des similarités (S31); sélectionner, parmi des étiquettes d'utilisateurs de l'ensemble voisin le plus proche, des étiquettes qui ne sont pas utilisées par l'utilisateur cible, et utiliser celles-ci en tant qu'étiquettes candidates de l'utilisateur cible (S32); calculer une corrélation de l'utilisateur cible à chaque étiquette candidate, et sélectionner un second nombre prédéfini d'étiquettes candidates en tant qu'étiquettes nouvellement ajoutées de l'utilisateur cible selon un ordre décroissant des corrélations (S33); et recommander à l'utilisateur cible des articles liés aux étiquettes originales et aux étiquettes nouvellement ajoutées selon les étiquettes originales et les étiquettes nouvellement ajoutées de l'utilisateur cible (S34). L'efficacité de la recommandation peut être améliorée.
PCT/CN2017/113724 2017-11-01 2017-11-30 Procédé de recommandation à filtrage collaboratif, dispositif électronique, et support d'informations lisible par ordinateur WO2019085120A1 (fr)

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