WO2021027149A1 - Procédé de recommandation d'extraction d'informations basé sur une similarité de portrait et dispositif et support de stockage - Google Patents

Procédé de recommandation d'extraction d'informations basé sur une similarité de portrait et dispositif et support de stockage Download PDF

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WO2021027149A1
WO2021027149A1 PCT/CN2019/117794 CN2019117794W WO2021027149A1 WO 2021027149 A1 WO2021027149 A1 WO 2021027149A1 CN 2019117794 W CN2019117794 W CN 2019117794W WO 2021027149 A1 WO2021027149 A1 WO 2021027149A1
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user
similarity
portrait
query
portraits
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PCT/CN2019/117794
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English (en)
Chinese (zh)
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刘利
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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
    • 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
    • G06F16/9536—Search customisation based on social or collaborative filtering

Definitions

  • This application relates to the field of data analysis technology, and in particular to an information retrieval recommendation method, device, system and computer-readable storage medium based on the similarity of user portraits.
  • CIR Collaborative Information Retrieval
  • the CIR collaborative information retrieval system can analyze user interaction history records to more effectively respond to subsequent user queries.
  • two users send the same query to the CIR system at the same time, because the goals and behavior characteristics of the two users may be different, they may be interested in two different document lists.
  • CIR faces a personalized Query recommended questions.
  • information retrieval is the main way for users to query and obtain information. It is a method and means to find information.
  • Information storage is the basis for information retrieval.
  • the information to be stored here includes original document data, pictures, videos, and audio. In order to achieve information retrieval, the original information must be converted into computer language and stored in the database, otherwise machine identification cannot be performed.
  • the retrieval system After the user enters the query request according to the intention, the retrieval system searches the database for information related to the query according to the user’s query request, calculates the similarity of the information through a certain matching mechanism, and converts the information in order from large to small Output.
  • the inventor realizes that the existing information retrieval methods are either relatively complicated, or have poor retrieval accuracy and insufficient personalization, resulting in poor recommendation effects and poor user experience.
  • This application provides a method, electronic device, system and computer-readable storage medium for information retrieval and recommendation based on the similarity of user portraits.
  • the main purpose of the method is to obtain the similarity of user portraits through the maximum matching of weighted bipartite graphs, and to obtain information between different users.
  • This method can dynamically build user communities in a collaborative information retrieval environment and apply it to personalized information retrieval, improve retrieval accuracy, and optimize user experience.
  • this application provides an information retrieval recommendation method based on the similarity of user portraits, which is applied to an electronic device, and the method includes:
  • the user is recommended for information retrieval.
  • the present application also provides an electronic device, the electronic device comprising: a memory and a processor, the memory includes an information retrieval recommendation program based on the similarity of portraits, and the information retrieval recommendation program based on the similarity of user portraits is processed by the processor.
  • the following steps are implemented during execution:
  • the user is recommended for information retrieval.
  • this application also provides an information retrieval recommendation system based on the similarity of portraits, including:
  • the user portrait similarity determination unit is used to obtain user portraits of different users and determine the user portrait similarity between user portraits
  • Dynamic community creation unit users create user dynamic communities based on the similarity of user portraits, so that users with similar portraits belong to the same user dynamic community;
  • the search recommendation unit is used to perform information search and recommendation for users according to the user's dynamic community and the user's query sentence.
  • the present application also provides a computer-readable storage medium.
  • the computer-readable storage medium includes an information retrieval recommendation program based on the similarity of user portraits.
  • the information retrieval recommendation program based on the similarity of user portraits is processed by the processor. When executed, the steps of the above information retrieval recommendation method based on the similarity of the user portrait are realized.
  • the method, device, system and computer-readable storage medium for information retrieval and recommendation based on the similarity of user portraits proposed in this application construct a weighted bipartite graph based on user portraits, and obtain the maximum weight between user portraits by using the maximum matching of the weighted bipartite graphs
  • the matching value can dynamically construct a user community based on the similarity of user portraits in a collaborative information retrieval environment, and perform personalized information retrieval recommendations based on the user community, which can improve user retrieval accuracy, optimize user experience, and achieve personalized recommendations.
  • FIG. 1 is a schematic diagram of an application environment of a preferred embodiment of an information retrieval recommendation method based on the similarity of user portraits according to the present application;
  • FIG. 2 is a schematic diagram of modules of a preferred embodiment of an information retrieval recommendation system based on the similarity of user portraits according to the present application;
  • FIG. 3 is a flowchart of a preferred embodiment of an information retrieval recommendation method based on the similarity of user portraits according to the present application
  • Figure 4 is a flowchart of a method for calculating the similarity of user portraits based on graph algorithms:
  • Figure 5 is a bipartite graph constructed based on user portraits of two different users.
  • This application provides an information retrieval and recommendation method based on the similarity of user portraits, which is applied to an electronic device 1.
  • FIG. 1 it is a schematic diagram of the application environment of the preferred embodiment of the information retrieval recommendation method based on the similarity of user portraits of this application.
  • the electronic device 1 may be a terminal device with arithmetic function, such as a server, a smart phone, a tablet computer, a portable computer, a desktop computer, and the like.
  • the electronic device 1 includes a processor 12, a memory 11, a network interface 14 and a communication bus 15.
  • the memory 11 includes at least one type of readable storage medium.
  • At least one type of readable storage medium may be a non-volatile storage medium such as flash memory, hard disk, multimedia card, card-type memory 11, and the like.
  • 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.
  • the readable storage medium may also be the external memory 11 of the electronic device 1, such as a plug-in hard disk or a smart memory card (Smart Media Card, SMC) equipped on the electronic device 1. , Secure Digital (SD) card, Flash Card, etc.
  • SD Secure Digital
  • the readable storage medium of the memory 11 is generally used to store the information retrieval recommendation program 10 based on the similarity of user portraits installed in the electronic device 1 and the like.
  • the memory 11 can also be used to temporarily store data that has been output or will be output.
  • the processor 12 may be a central processing unit (CPU), a microprocessor or other data processing chip, which is used to run the program code or process data stored in the memory 11, for example, to perform execution based on user profile Similarity information retrieval recommendation program 10 etc.
  • CPU central processing unit
  • microprocessor or other data processing chip, which is used to run the program code or process data stored in the memory 11, for example, to perform execution based on user profile Similarity information retrieval recommendation program 10 etc.
  • the network interface 14 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface), and is generally used to establish a communication connection between the electronic device 1 and other electronic devices.
  • the communication bus 15 is used to realize the connection and communication between these components.
  • FIG. 1 only shows the electronic device 1 with the components 11-15, but it should be understood that it is not required to implement all the illustrated components, and more or fewer components may be implemented instead.
  • the electronic device 1 may also include a user interface.
  • the user interface may include an input unit such as a keyboard (Keyboard), a voice input device such as a microphone (microphone) and other devices with voice recognition functions, and a voice output device such as audio, earphones, etc.
  • the user interface may also include a standard wired interface and a wireless interface.
  • the electronic device 1 may also include a display, and the display may also be called a display screen or a display unit.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an organic light-emitting diode (Organic Light-Emitting Diode, OLED) touch device.
  • OLED Organic Light-Emitting Diode
  • the display is used for displaying information processed in the electronic device 1 and for displaying a visualized user interface.
  • the electronic device 1 further includes a touch sensor.
  • the area provided by the touch sensor for the user to perform a touch operation is called a touch area.
  • the touch sensor described here may be a resistive touch sensor, a capacitive touch sensor, or the like.
  • the touch sensor includes not only a contact type touch sensor, but also a proximity type touch sensor and the like.
  • the touch sensor may be a single sensor, or may be, for example, a plurality of sensors arranged in an array.
  • the area of the display of the electronic device 1 may be the same as or different from the area of the touch sensor.
  • the display and the touch sensor are stacked to form a touch display screen. The device detects the touch operation triggered by the user based on the touch screen.
  • the electronic device 1 may also include a radio frequency (RF) circuit, a sensor, an audio circuit, etc., which will not be repeated here.
  • RF radio frequency
  • the memory 11 as a computer storage medium may include an operating system and an information retrieval recommendation program 10 based on the similarity of user portraits; the processor 12 executes the user-based information stored in the memory 11
  • the image similarity information retrieval recommendation program 10 implements the following steps:
  • the user is recommended for information retrieval.
  • the user portraits of different users are obtained, and the user portrait similarity between the user portraits is determined to be obtained by the user portrait similarity calculation method based on the graph algorithm;
  • the user portrait similarity calculation method based on the graph algorithm includes the following steps:
  • P(X) is the user portrait of user X
  • P(Y) is the user portrait of user Y
  • the vertex e of is connected to the vertex é of P(Y) through the edge (e, é);
  • the user portrait similarity of user X and user Y is obtained according to the maximum weighted matching value.
  • the user portrait P(X) of user X is stored as:
  • the user portrait P(Y) of user Y is stored as:
  • the vertex e of the user portrait P(X) includes a corresponding first query element and a first document element
  • the vertex e of the user portrait P(Y) includes a corresponding second query element and a second document element
  • the process of obtaining the similarity between the vertex e of the user portrait P(X) and the vertex é of the user portrait P(Y) includes:
  • the similarity between the vertex e and the vertex é is determined based on the first similarity and the second similarity.
  • the first similarity between the first query element and the second query element is obtained through edit distance algorithm, Jaccard coefficient algorithm, TF algorithm, TFIDF algorithm, or Word2Vec algorithm;
  • the second similarity between the first document element and the second document element is obtained by the TFIDF algorithm or the space vector-based cosine algorithm.
  • the user portrait P(X) of the user X includes elements A, B, C, D, and E, wherein the elements A, B, C, D, and E include the first query element and the first document element;
  • the user portrait P(Y) of user Y includes elements 1, 2, 3, 4, and 5, where elements 1, 2, 3, 4, and 5 include the second query element and the second document element;
  • Step 1 Obtain all weighted matching values of the weighted bipartite graph by the following formula
  • M 1 w(A,1)+w(B,3)+w(C,2)+w(D,4)+w(E,5)
  • M 2 w(A,1)+w(B,3)+w(C,5)+w(D,4)+w(E,2)
  • M 2 w(A,1)+w(B,4)+w(C,2)+w(D,3)+w(E,5)
  • M 2 w(A,1)+w(B,4)+w(C,5)+w(D,3)+w(E,2)
  • w(i, j) represents the similarity between element i and element j or the weight of edge ij;
  • Step 2 Determine the maximum weighted matching value from all weighted matching values.
  • a user community can be created based on the user portrait similarity between the user P(X) and the user P(Y), and the user query results can be ranked and recommended according to the created user community.
  • the steps of querying based on the similarity of user portraits between user P(X) and user P(Y) include:
  • Step 1 Find a historical query record A similar to query q.
  • U m represents the user
  • q m is the query of the user U m
  • D qm is all documents related to the query q m
  • P(U) is the user portrait of the user U
  • P(U i ) is the user portrait of the user i
  • S(P(u),P(U i )) is the similarity of user portrait between user U and user I
  • s(q,q 1 ) is the similarity between sentence q and sentence qi, the above similarity
  • Both can be obtained by the user portrait similarity calculation method based on graph algorithm.
  • Step 2 Calculate all document collections related to query q.
  • Step 3 For each document d in the corpus, calculate the similarity between d and q to obtain the similarity r(d, q);
  • Step 4 Calculate the final ranking of each document in the corpus:
  • a and b are setting coefficients.
  • Step 5 According to the final ranking of the documents, the documents can be sorted to construct an output list. According to the output list, the sentence q that the user U needs to query can be queried and output.
  • the electronic device 1 proposed in the above embodiment obtains the similarity between user portraits through the maximum matching of the weighted bipartite graph, and can dynamically construct a user community based on the similarity of user portraits in a collaborative information retrieval environment, and is personalized according to the user community Information retrieval recommendation can improve user retrieval accuracy, optimize user experience, and achieve personalized recommendation.
  • this application also provides an information retrieval recommendation system based on the similarity of user portraits.
  • FIG. 2 it is a program module diagram of a preferred embodiment of the information retrieval recommendation system based on the similarity of user portraits in the embodiment of this application.
  • the information retrieval recommendation system based on the similarity of user portraits can be divided into:
  • the user portrait similarity determination unit 110 is configured to obtain user portraits of different users and determine the user portrait similarity between the user portraits;
  • the dynamic community creation unit 120 the user creates a user dynamic community based on the similarity of user portraits, so that users with similar portraits belong to the same user dynamic community;
  • the search recommendation unit 130 is configured to perform information search and recommendation for users based on the user's dynamic community and the user's query sentence.
  • the user portrait similarity determination unit 110 further includes:
  • the user portrait storage module 111 is configured to store the user portrait P as a collection related to coordinates (q, D q ); where q represents any query record of the user, and D q represents all documents related to the query record q;
  • the weighted bipartite graph construction module 112 is used to construct a weighted bipartite graph based on the user profile P(X) and the user profile P(Y) to be processed; where P(X) is the user profile of user X, and P(Y) is the user User portrait of Y, vertex e of P(X) is connected to vertex é of P(Y) through edge (e, é);
  • the similarity acquisition module 113 is configured to acquire the similarity between the vertex e of the user portrait P(X) and the vertex é of the user portrait P(Y) based on the weighted bipartite graph;
  • the weight determination module 114 is configured to determine the weight of the edge (e, é) according to the similarity between the vertex e of P(X) and the vertex é of P(Y);
  • the maximum weighted matching value obtaining module 115 is configured to obtain the maximum weighted matching value between the user portrait P(X) and the user portrait P(Y) based on the weight of the edge (e, é);
  • the user portrait similarity determination module 116 is configured to obtain the user portrait similarity of the user X and the user Y according to the maximum weighted matching value.
  • the user portrait P(X) of user X is stored as:
  • the user portrait P(Y) of user Y is stored as:
  • the vertex e of the user portrait P(X) includes the corresponding first query element and the first document element
  • the vertex é of the user portrait P(Y) includes the corresponding second query element and the second document element
  • the similarity acquisition module 113 includes:
  • the query element and document element similarity acquisition module 1131 configured to acquire the first similarity between the first query element and the second query element, and to acquire the second similarity between the first document element and the second document element;
  • the similarity determination module 1132 between vertices is used to determine the similarity between the vertex e and the vertex e based on the first similarity and the second similarity.
  • the query element and document element similarity acquisition module 1131 includes:
  • the first similarity acquisition module is used to acquire the first similarity between the first query element and the second query element through the edit distance algorithm, the Jaccard coefficient algorithm, the TF algorithm, the TFIDF algorithm, or the Word2Vec algorithm;
  • the second similarity acquisition module is configured to acquire the second similarity between the first document element and the second document element through the TFIDF algorithm or the space vector-based cosine algorithm.
  • the user portrait P(X) of user X includes elements A, B, C, D, and E, where elements A, B, C, D, and E include the first query element and the first document element;
  • the user portrait P(Y) of user Y includes elements 1, 2, 3, 4, and 5, where elements 1, 2, 3, 4, and 5 include the second query element and the second document element;
  • Step 1 Obtain all weighted matching values of the weighted bipartite graph by the following formula
  • M 1 w(A,1)+w(B,3)+w(C,2)+w(D,4)+w(E,5)
  • M 2 w(A,1)+w(B,3)+w(C,5)+w(D,4)+w(E,2)
  • M 2 w(A,1)+w(B,4)+w(C,2)+w(D,3)+w(E,5)
  • M 2 w(A,1)+w(B,4)+w(C,5)+w(D,3)+w(E,2)
  • w(i, j) represents the similarity between element i and element j or the weight of edge ij;
  • Step 2 Determine the maximum weighted matching value from all weighted matching values.
  • this application also provides an information retrieval recommendation method based on the similarity of user portraits.
  • FIG. 3 it is a flowchart of a preferred embodiment of an information retrieval recommendation method based on the similarity of user portraits according to this application.
  • the method can be executed by a device, and the device can be implemented by software and/or hardware.
  • the information retrieval recommendation method based on the similarity of user portraits includes the following steps:
  • Step S11 Obtain user portraits of different users, and determine the user portrait similarity between the user portraits.
  • Step S12 Create a user dynamic community based on the similarity of user portraits, so that users with similar portraits belong to the same user dynamic community.
  • Step S13 Perform information search and recommendation on the user according to the user's dynamic community and the user's query sentence.
  • step S11 further includes the following steps:
  • Step S101 Store the user portrait P as a collection related to the coordinates (q, D q ); where q represents any query record of the user, and D q represents all documents related to the query record q.
  • user portraits are also known as user roles.
  • user portraits As an effective tool for delineating target users, contacting user demands and design directions, user portraits have been widely used in various fields. In the process of actual operation, we often use the most simple and life-like words to connect users' attributes, behaviors and expectations. As virtual representatives of actual users, the user roles formed by user portraits are not constructed outside of the product and the market. The user roles formed need to have representative performance to represent the main audience and target groups of the product.
  • the user portrait P(X) of user X can be stored as:
  • the user portrait P(Y) of user Y can be stored as:
  • the User Profile Similarity (UPS) between User X and User Y is to calculate the similarity between the above two sets of P(x) and P(y).
  • Step S102 Construct a weighted bipartite graph based on the user portrait P(X) and user portrait P(Y) to be processed; where P(X) is the user portrait of user X, P(Y) is the user portrait of user Y, and P The vertex e of (X) is connected to the vertex é of P(Y) through the edge (e, é).
  • bipartite graph is also called bipartite graph, which is a special model in graph theory.
  • the elements of the user portrait P(X) form part of the graph G, and the elements of P(Y) form another part of the graph.
  • Each vertex e of P(X) is connected to each vertex é of P(Y) by an edge (e, é).
  • the weight of the edge (e, é) is equal to the similarity between the vertices (or elements) e and é.
  • the weight of the edge (e, é) is related to the element type, and the element type includes query or document.
  • the vertex e of the user portrait P(X) includes a corresponding first query element and a first document element
  • the vertex e of the user portrait P(Y) includes a corresponding second query element and a second document element
  • the process of obtaining the similarity between the vertex e of the user portrait P(X) and the vertex é of the user portrait P(Y) includes:
  • Step S103 Obtain the similarity between the vertex e of the user portrait P(X) and the vertex é of the user portrait P(Y) based on the weighted bipartite graph.
  • each vertex e of the user portrait P(X) includes a corresponding query element and document element
  • each vertex of the user portrait P(Y) also includes a corresponding query element and document element.
  • the difference between vertex e and vertex é is obtained.
  • the similarity between the elements based on the similarity between the query vertices of the user profile P(x) and the user profile P(Y) and the similarity between the vertices of each document, we can determine all the vertices e and é The similarity.
  • the current method for obtaining the similarity of query sentences mainly includes: Edit distance algorithm, Jaccard coefficient algorithm, TF algorithm, TFIDF algorithm, Word2Vec algorithm, etc.
  • Edit Distance in English
  • Levenshtein distance algorithm refers to the minimum number of edit operations required to convert two strings from one to the other. If their distance is greater, they The more different.
  • the permitted editing operations include replacing one character with another, inserting a character, deleting a character, etc.
  • Jaccard coefficient called Jaccard index in English
  • Jaccard similarity coefficient which is used to compare the similarity and difference between a limited sample set.
  • the calculation method of the Jaccard coefficient is very simple. It is the value obtained by dividing the intersection of two samples by the union. When the two samples are exactly the same, the result is 1, and when the two samples are completely different, the result is 0.
  • the similarity calculation methods between the documents of the user profile P(X) and the user profile P(Y) mainly include the TFIDF algorithm and the cosine algorithm based on space vectors.
  • the first similarity between the first query element and the second query element is obtained by the edit distance algorithm, the Jacquard coefficient algorithm, the TF algorithm, the TFIDF algorithm, or the Word2Vec algorithm; between the first document element and the second document element
  • the second similarity of is obtained by TFIDF algorithm or cosine algorithm based on space vector.
  • Step S104 Determine the weight of the edge (e, é) according to the similarity between the vertex e of P(X) and the vertex é of P(Y).
  • the weight of the edge (e, é) can be set equal to the similarity between the vertex e of P(X) and the vertex é of P(Y).
  • Step S105 Obtain the maximum weighted matching value between the user portrait P(X) and the user portrait P(Y) based on the weight of the edge (e, é).
  • the maximum matching of the bipartite graph mainly refers to: given a bipartite graph G, in a subgraph M of the bipartite graph G, any two edges in the edge set of M are not attached to the same vertex, then M is called a match. Choosing such a subset with the largest number of edges is called the maximum matching problem of the graph. If in a match, every vertex in the graph is associated with an edge in the graph, then the match is called a complete match , Also known as complete matching.
  • the user portrait P(X) of user X includes elements A, B, C, D, and E, where A, B, C, D, and E contain the first query element and the first document element, and the user portrait of user Y P(Y) contains elements 1, 2, 3, 4, and 5, of which 1, 2, 3, 4, and 5 contain the second query element and the second document element.
  • the user profile P(X) and the user profile P(Y ) The constructed bipartite graph is shown in Figure 4.
  • the weighted matching value of the maximum matching situation is calculated by the following formula:
  • M 1 w(A,1)+w(B,3)+w(C,2)+w(D,4)+w(E,5)
  • M 2 w(A,1)+w(B,3)+w(C,5)+w(D,4)+w(E,2)
  • M 2 w(A,1)+w(B,4)+w(C,2)+w(D,3)+w(E,5)
  • M 2 w(A,1)+w(B,4)+w(C,5)+w(D,3)+w(E,2)
  • w(i, j) represents the similarity between element i and element j or the weight of edge ij; for example, w(A, 1) represents the similarity between element A and element 1, which also represents the edge
  • w(B,3), w(C,2)...w(E,5), etc. are similar.
  • the maximum weighted matching value is determined from all the weighted matching values.
  • the maximum weighted matching value is 3.5.
  • Step S106 Acquire the user portrait similarity of the user X and the user Y according to the maximum weighted matching value.
  • a user community can be created based on the user portrait similarity between the user P(X) and the user P(Y), and the user query results can be performed according to the created user community. Sort recommendation.
  • the steps of querying based on the similarity of user portraits between user P(X) and user P(Y) include:
  • Step 1 Find a historical query record A similar to query q.
  • U m represents the user
  • q m is the query of the user U m
  • D qm is all documents related to the query q m
  • P(U) is the user portrait of the user U
  • P(U i ) is the user portrait of the user i
  • S(P(U),P(U i )) is the similarity of user portrait between user U and user I
  • s(q,q 1 ) is the similarity between sentence q and sentence qi, the above similarity
  • Both can be obtained by the user portrait similarity calculation method based on graph algorithm.
  • Step 2 Calculate all document collections related to query q.
  • Step 3 For each document d in the corpus, calculate the similarity between d and q to obtain the similarity r(d, q);
  • Step 4 Calculate the final ranking of each document in the corpus:
  • a and b are setting coefficients.
  • Step 5 According to the final ranking of the documents, the documents can be sorted to construct an output list. According to the output list, the sentence q that the user U needs to query can be queried and output.
  • the weighted bipartite graph maximum matching method is used to obtain the similarity between user portraits, and the user community can be dynamically constructed based on the similarity of user portraits in the collaborative information retrieval environment, and according to users
  • the community’s personalized information retrieval recommendation can improve user retrieval accuracy, optimize user experience, and achieve personalized recommendation.
  • the embodiment of the present application also proposes a computer-readable storage medium.
  • the computer-readable storage medium includes an information retrieval recommendation program based on the similarity of user portraits.
  • the information retrieval recommendation program based on the similarity of user portraits is implemented when the processor is executed. Do as follows:
  • the user is recommended for information retrieval.
  • the specific implementation of the computer-readable storage medium of the present application is substantially the same as the specific implementation of the above-mentioned information retrieval recommendation method, electronic device, and system based on the similarity of user portraits, and will not be repeated here.

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Abstract

L'invention concerne un procédé de recommandation d'extraction d'informations basé sur une similarité de portrait, un dispositif, un système et un support de stockage, le procédé consistant à : obtenir des portraits d'utilisateur de différents utilisateurs, et déterminer la similarité de portrait d'utilisateur entre les portraits d'utilisateur (S11) ; créer une communauté d'utilisateurs dynamique sur la base de la similarité de portrait d'utilisateur, et permettre à des utilisateurs ayant des portraits similaires d'être groupés dans la même communauté d'utilisateurs dynamique (S12) ; et effectuer une recommandation d'extraction d'informations sur l'utilisateur en fonction de la communauté d'utilisateurs dynamique et d'une instruction d'interrogation de l'utilisateur (S13). En calculant la similarité entre les portraits d'utilisateur, la similarité entre différents utilisateurs peut être obtenue, et une extraction et une recommandation d'informations personnalisées peuvent être réalisées.
PCT/CN2019/117794 2019-08-14 2019-11-13 Procédé de recommandation d'extraction d'informations basé sur une similarité de portrait et dispositif et support de stockage WO2021027149A1 (fr)

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CN201910748591.3 2019-08-14
CN201910748591.3A CN110598123B (zh) 2019-08-14 2019-08-14 基于画像相似性的信息检索推荐方法、装置及存储介质

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