WO2019109724A1

WO2019109724A1 - Item recommendation method and device

Info

Publication number: WO2019109724A1
Application number: PCT/CN2018/109590
Authority: WO
Inventors: 唐睿明; 何秀强; 钮敏哲; 张伟楠; 俞勇
Original assignee: 华为技术有限公司
Priority date: 2017-12-07
Filing date: 2018-10-10
Publication date: 2019-06-13
Also published as: CN109903103A; CN109903103B

Abstract

Embodiments of the present invention disclose an item recommendation method and device, pertaining to the technical field of computers. The method comprises: acquiring attribute data of a target user and attribute data of multiple candidate items; processing the attribute data of the target user and the attribute data of the multiple candidate items to generate a target data set, the target data set comprising an identifier of the target user and a corresponding first target interaction node list, and an identifier of each candidate item among the multiple candidate items and a corresponding second target interaction node list; inputting the target data set into a scoring model to obtain scores given by the target user with respect to the multiple candidate items, wherein the scoring model is obtained according to attribute data of multiple users, attribute data of multiple items, and scoring data training; and determining a target recommended item according to the scores given by the target user with respect to the multiple candidate items. The present invention improves efficiency when a user selects an item.

Description

Method and device for recommending articles

The present application claims priority to Chinese Patent Application Serial No. No. No. No. No. No. No. No. No. No. No. No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No No

Technical field

The present application relates to the field of computer technology, and in particular, to a method and apparatus for recommending an item.

Background technique

With the development of computer technology and Internet technology, mobile phones, computers and other terminals have been widely used, and the types of applications on the corresponding terminals are more and more and more functions are becoming more and more abundant. People can make purchases through the shopping app installed in the terminal, and watch movies through the video playback application installed in the terminal.

Before people can operate on an item, they can first select the item (product or movie, etc.) to be processed. Specifically, the user may trigger the terminal to send an item list obtaining request to the server by operation, and after receiving the item list obtaining request, the server may send the item list of each item stored in the server to the terminal. After receiving the item list, the terminal can display it, and the user can browse each item in the item list one by one to determine the final favorite item.

In the process of implementing the present application, the inventors found that the prior art has at least the following problems:

Based on the above processing method, when the user wants to select an item, it needs to be selected in the item list sent by the server, and often the number of items in the item list is relatively large, thereby causing the user to select the item with low efficiency.

Summary of the invention

In order to solve the problem that the efficiency of the user selecting an item exists in the related art, the embodiment of the present invention provides a method and an apparatus for recommending an item. The technical solution is as follows:

In a first aspect, a method for recommending an item is provided, the method comprising: obtaining attribute data of a target user and attribute data of a plurality of candidate items, the attribute data of the target user includes an identifier of the target user, and attribute data of each candidate item Include an identifier of the corresponding candidate item; process the attribute data of the target user and the attribute data of the plurality of candidate items to generate a target data set, where the target data set includes the identifier of the target user and the corresponding target first interaction node list, and multiple candidates An identifier of each candidate item in the item and a corresponding target second interaction node list, the target first interaction node list is used to represent interaction information of the target user with other users or items, and the target second interaction node list is used to represent the candidate item and The interaction information of other items or users; input the target data set into the scoring model, and obtain the scoring of the plurality of candidate items by the target user, wherein the scoring model is trained according to the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data. , multiple users including target users, multiple uses The attribute data of each user includes the identifier of the corresponding user, and the plurality of items includes a plurality of candidate items, and the attribute data of each item of the plurality of items includes the identifier of the corresponding item, and the scoring data includes each of the plurality of users. Scoring one or more items of the plurality of items; determining the target recommended items according to the scoring of the plurality of candidate items by the target user.

In the solution shown in the embodiment of the present invention, the server may have the function of recommending an item. Specifically, the server may acquire the attribute data of the target user and the attribute data of the plurality of candidate items in the candidate set. Further, the attribute data of the target user and the attribute data of the plurality of candidate items may be processed to obtain a user including the target user. Corresponding target first interaction node list, identifier of each candidate item in the plurality of candidate items, and corresponding target second interaction node list, wherein the target first interaction node list may be used to represent the target user and other users or items The interaction information, that is, the target first interaction node list may include identifiers of other users or items that the target user history has interacted with, and the target second interaction node list may be used to indicate interaction information between the candidate items and other items or users, that is, the target number The second interactive node list may contain other items or user's identification that the candidate item history has interacted with.

The scoring model may be pre-stored in the server, wherein the scoring model may be obtained by the server according to the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data, wherein the plurality of users include the target user, and the plurality of items include multiple Candidates. The server may predict, by the scoring model, the scoring of each candidate item of the plurality of candidate items by the target user. Specifically, the server generates the identifier of the target user and the corresponding target first interactive node list, and each candidate item of the plurality of candidate items. After the identifier and the target data set of the corresponding target second interaction node list, the target data set can be input into the scoring model, and the target user can score the plurality of candidate items. Further, the server can be based on the target user to the plurality of candidate items. The score is determined, among the plurality of candidate items, the target recommended item to be recommended to the target user. In this way, the target user can select the desired item among the target recommended items recommended by the server, and does not need to select among all the items stored in the server, thereby improving the efficiency of the user selecting the item. In addition, the server utilizes the interaction information of the target user with other users or items (ie, the target first interactive node list) and the interaction information of each candidate item with other items or users when predicting the target user's scoring of each candidate item. (ie, the target second interactive node list), thereby improving the accuracy of the score obtained.

In a possible implementation manner, the attribute data of the target user further includes one or more of the following data: gender, height, weight, age, occupation, income, hobbies, education, and attribute data of each candidate item. Also included is one or more of the following data: brand, color, size, price, comment, taste, shelf life, icon.

In a possible implementation manner, the attribute data of the target user and the attribute data of the plurality of candidate items are processed to generate the target data set, including: each user among the plurality of pre-recorded users according to the identifier of the target user Determining, in the target first interaction node list corresponding to the target, determining a target first interaction node list corresponding to the target user, and corresponding to the identifier of each candidate item among the plurality of candidate items recorded in advance according to the identifier of each candidate item Determining, in the target second interaction node list, a target second interaction node list corresponding to each candidate item; according to the identifier of the target user, the target first interaction node list corresponding to the target user, the identifier of each candidate item, and each candidate A target second interactive node list corresponding to the item, and a target data set is generated.

In the solution shown in the embodiment of the present invention, the target first interaction node list corresponding to the identifier of each user of the plurality of users and the target second interaction corresponding to the identifier of each candidate item of the plurality of candidate items may be pre-stored in the server. a node list, wherein the server may record, in the form of a table, a target first interaction node list corresponding to the identifier of each user and a target second interaction node list corresponding to the identifier of each candidate item, or may record in the form of a bipartite graph a target first interaction node list corresponding to the identifier of each user and a target second interaction node list corresponding to the identifier of each candidate item. After the server obtains the identifier of the target user and the identifier of each candidate item, the target first interaction node corresponding to the target user may be determined in the target first interaction node list corresponding to the identifier of each user among the plurality of pre-recorded users. a list, and the target second interaction node list corresponding to each candidate item may be determined in the target second interaction node list corresponding to the identifier of each candidate item among the plurality of candidate items recorded in advance. After determining the target first interaction node list corresponding to the target user and the target second interaction node list corresponding to each candidate item, the server may generate the identifier including the target user and the corresponding target first interaction node list, and each candidate item. Identifying and corresponding target data sets of the target second interaction node list, wherein each target data in the target data set may include the identifier of the target user and the corresponding target first interaction node list, the identifier of the candidate item j, and the corresponding target number The second interactive node list, the candidate item j is any one of the plurality of candidate items.

In a possible implementation manner, the scoring model includes a feature learning model, a feedback learning model, and a neural network model;

The target data set is input into the scoring model to obtain the scoring of the plurality of candidate items by the target user, including: inputting the identifier of the target user in the target data set and the identifier of the candidate item j into the feature learning model, and obtaining the feature vector corresponding to the target user. And a feature vector corresponding to the candidate item j, and the target first interaction node list corresponding to the target user in the target data set and the target second interaction node list corresponding to the candidate item j are input into the feedback learning model, and the implicit correspondence corresponding to the target user is obtained. Feedback is implicit feedback corresponding to the candidate item j, wherein the item j is any one of the plurality of candidate items; the feature vector corresponding to the target user, the feature vector corresponding to the candidate item j, and the implicit feedback corresponding to the target user The implicit feedback corresponding to the candidate item j is input to the neural network model, and the target user is scored for the candidate item j.

The feature vector corresponding to the target user may be a vector for characterizing the feature (or characteristic) of the user itself. The feature vector corresponding to the candidate item j may be a vector for characterizing the feature (or characteristic) of the candidate item j itself.

In the solution shown in the embodiment of the present invention, the scoring model may include a feature learning model, a feedback learning model, and a neural network model, wherein the feature learning model may be a feature vector for learning the target user and each candidate item, and the feature learning model The model parameters may include a user feature matrix and an item feature matrix, wherein the user feature matrix is composed of feature vectors of each user of the plurality of users (ie, each row vector of the user feature matrix is a feature vector of the corresponding user, respectively, and the user feature The number of rows of the matrix is the number of multiple users. The item feature matrix is composed of the feature vectors of each of the plurality of items (ie, each row of the item feature matrix is a feature vector of the corresponding item, and the item feature matrix The number of rows is the number of items). After obtaining the target data set, the server may input the identifier of the target user and the identifier of the candidate item j into the feature learning model to obtain the feature vector corresponding to the target user and the feature vector corresponding to the candidate item j. Specifically, after the server inputs the identifier of the target user and the identifier of the candidate item j into the feature learning model, the feature vector corresponding to the target user is extracted in the user feature matrix by the feature learning model according to the identifier of the target user and the identifier of the candidate item j. The feature vector corresponding to the candidate item j is extracted in the item feature matrix, and the feature vector corresponding to the target user and the feature vector corresponding to the candidate item j are obtained.

The feedback learning model may be implicit feedback for learning the target user and each candidate item, and the model parameters of the feedback learning model may include a user feedback matrix (which may be represented by Y) and an item feedback matrix (which may be represented by X), wherein The user feedback matrix may be composed of feedback vectors (each row vector in the user feedback matrix represents a feedback vector corresponding to one node), and the item feedback matrix may be composed of feedback vectors (each row vector in the item feedback matrix represents one) The feedback vector corresponding to the node). After determining the target first interactive node list (which may be represented by R) corresponding to the target user (which may be represented by _k ), and selecting the target second interactive node list corresponding to the candidate item j (which may be represented by R _j ), The feedback learning model is input, and the implicit feedback corresponding to the target user k and the implicit feedback corresponding to the candidate item j are obtained. Specifically, after the server inputs the target first interaction node list corresponding to the target user k into the feedback learning model, the feedback learning model may extract multiple feedback vectors corresponding to the target first interaction node list in the user feedback matrix (wherein the feedback vector) The number of nodes is the number of nodes included in the target first interactive node list), and the feedback vector corresponding to the target user k is obtained. After obtaining the feedback vector corresponding to the target user k, multiple feedback vectors may be added to obtain implicit feedback corresponding to the target user k. The specific processing of obtaining the implicit feedback corresponding to the candidate item j may be as follows: after the server inputs the target second interactive node list corresponding to the candidate item j into the feedback learning model, the target second interaction node may be extracted in the item feedback matrix by using the feedback learning model. A plurality of feedback vectors corresponding to the list (wherein the number of feedback vectors is the number of nodes included in the target second interactive node list), and a feedback vector corresponding to the candidate item j is obtained. After the feedback vector corresponding to the candidate item j is obtained, a plurality of feedback vectors may be added to obtain an implicit feedback corresponding to the candidate item j.

After obtaining the feature vector corresponding to the target user k, the feature vector corresponding to the candidate item j, the implicit feedback corresponding to the target user k, and the implicit feedback corresponding to the candidate item j, the server may input the neural network model into the target user candidate. The score of item j.

In a possible implementation manner, the target first interaction node list includes a multi-level target first interaction node list, and the target second interaction node list corresponding to each candidate item includes a multi-level target second interaction node list, and the multi-level target The first interactive node list in the first interactive node list is used to represent the interaction information of the target user and the item, and the even-order target in the multi-level target first interactive node list is used to represent the target user and other users. Interactive information, multi-level target second interactive node list odd-numbered target second interactive node list is used to represent candidate item and user interaction information, multi-level target second interactive node list in even-order target second interactive node list The information indicating the interaction between the candidate item and the other item; the target first interactive node list corresponding to the target user in the target data set and the target second interactive node list corresponding to the candidate item j are input into the feedback learning model to obtain the hidden corresponding to the target user. Implicit feedback corresponding to candidate item j, including: target According to the multi-level target first interaction node list corresponding to the centralized target user and the multi-level target second interaction node list corresponding to the candidate item j, the feedback learning model is input, and the implicit feedback corresponding to the target user and the hidden corresponding to the candidate item j are obtained. Feedback.

In the solution shown in the embodiment of the present invention, when the server predicts the target user to score the candidate item j, the server may also use the multi-level target first interaction node list corresponding to the target user, and the multi-level target second interaction node corresponding to the candidate item j. a list, wherein the multi-level target first interaction node list may be a first-order target first interaction node list, a second-order target first interaction node list, ..., an A-order target first interaction node list, and a multi-level user feedback matrix may be Including first-order user feedback matrix, second-order user feedback matrix, ..., A-order user feedback matrix, A is a preset value (such as A is 3), A is the default target user can reach in the user-item map The maximum number of steps, the multi-level target second interactive node list may be a first-order target second interactive node list, a second-order target second interactive node list, ..., a B-order target second interactive node list, a multi-order item feedback matrix It may include a first-order item feedback matrix, a second-order item feedback matrix, ..., a B-order item feedback matrix, B is a preset value, and B is a preset candidate item j in the user-object The maximum number of steps to reach the two figures, wherein, A and B may be the same or different. The first-order user feedback matrix can be Y ¹ , and each row vector in the first-order user feedback matrix can be a vector representation of the corresponding item as a node in the first-order target first interaction node list, and a second-order user feedback matrix can Expressed by Y ² , each row vector in the second-order user feedback matrix may be a vector representation when the corresponding user is a node in the second-order target first interaction node list, and so on. The first-order item feedback matrix can be X ¹ , and each row vector in the first-order item feedback matrix can be a vector representation of the corresponding user as a node in the first-order target second interactive node list, and a second-order item feedback matrix can Expressed by X ² , each row vector in the second-order item feedback matrix may be a vector representation of the corresponding item as a node in the second-order target second interactive node list, and so on.

For this situation, the server may input the multi-level target first interaction node list corresponding to the target user in the target data set and the multi-level target second interaction node list corresponding to the candidate item j into the feedback learning model to obtain the target user corresponding Implicit feedback and implicit feedback corresponding to candidate j. Specifically, for each target, the first interactive node list

(a = 1,2, ..., A ), the server learning model by feedback, the user feedback matrix Y ^a step, a first interaction extraction target node list

Corresponding feedback vector. The server may select the feedback vector corresponding to the first interaction node list of each target object corresponding to the target user according to the above manner, and then add all the selected feedback vectors to obtain the implicit feedback corresponding to the target user. List of second-order interaction nodes per target for the candidate item j

(b=1, 2, ..., B), the server can select the target second interactive node list in the item feedback matrix X ^b by feedback learning model

Corresponding feedback vector, obtaining a target second interactive node list corresponding to the candidate item j

Corresponding feedback vector. The server may select the feedback vector corresponding to the second interactive node list of each target object corresponding to the candidate item j according to the above manner, and further, all the selected feedback vectors may be added to obtain the implicit feedback corresponding to the candidate item j. In this way, when predicting the target user's scoring of each candidate item, the historical interaction information corresponding to the target user and the historical interaction information of each order of each candidate item are utilized, thereby improving the predicted target user-to-candidate item. The accuracy of the score.

In a possible implementation manner, the model parameters of the feedback learning model include: a weight of a feedback vector of each of the plurality of users, a weight of a feedback vector of each of the plurality of items; and a target user in the target data set Corresponding target first interactive node list and target second interactive node list corresponding to the candidate item j, input a feedback learning model, and obtain implicit feedback corresponding to the target user and implicit feedback corresponding to the candidate item j, including: concentrating the target data The identifier of the target user and the corresponding target first interaction node list, the identifier of the candidate item j, and the corresponding target second interaction node list, input a feedback learning model, and obtain implicit feedback corresponding to the target user and the hidden corresponding to the candidate item j Feedback.

In the solution shown in the embodiment of the present invention, the model parameter for the feedback learning model further includes a weight of a feedback vector of each of the plurality of users and a weight of a feedback vector of each of the plurality of items, and the server may target the The identifier of the target user in the data set and the corresponding target first interaction node list, the identifier of the candidate item j, and the corresponding target second interaction node list, input the feedback learning model, and obtain the implicit feedback corresponding to the target user and the candidate item j correspondingly. Implicit feedback. Specifically, the server may determine the feedback vector corresponding to the target user and the feedback vector corresponding to the candidate item j by using the learning model according to the method for determining the feedback vector corresponding to the target user and the candidate item j. Then, the server may weight and process the feedback vector corresponding to the target user k by feeding back the weight of the feedback vector of the target user in the learning model (which may be represented by Φ _kt ) according to the identifier of the target user and the identifier of the candidate item j. The implicit feedback corresponding to the target user, and by weighting the feedback vector of the candidate item j in the feedback learning model (which can be represented by Ω _vj ), the feedback vector corresponding to the candidate item j is weighted and processed to obtain the hidden corresponding to the candidate item j. Feedback. In this way, when predicting the target user's scoring of each candidate item, the weight of the feedback vector of the target user and the weight of the feedback vector of each candidate item are introduced, thereby improving the predicted target user's scoring of the candidate item. accuracy.

In a possible implementation manner, determining the target recommended item according to the scoring of the plurality of candidate items by the target user, including: determining, according to the scoring of the plurality of candidate items by the target user, determining that the corresponding scoring meets the target recommendation of the preset recommended condition. article.

In the solution shown in the embodiment of the present invention, the server may pre-store the preset recommendation condition, and after the server obtains the score of the plurality of candidate items by the target user, the selected scores may be selected among the plurality of candidate items to satisfy the preset recommendation condition. Target recommended items.

In a possible implementation manner, determining, according to the scoring of the plurality of candidate items by the target user, determining that the corresponding scoring meets the target recommended item that meets the preset recommendation condition, including: determining, according to the scoring of the plurality of candidate items by the target user, determining corresponding The maximum number of target recommended items is scored; or, according to the target user's scoring of the plurality of candidate items, the target recommended item whose score is greater than the preset score threshold is determined.

In the solution shown in the embodiment of the present invention, after the server determines that the target user scores the plurality of candidate items, the plurality of candidate items may be sorted according to the order of the corresponding scores, and then the ranking is advanced. A predetermined number of candidate items are determined as target recommended items. Alternatively, a preset score threshold may be pre-stored in the server. After the server determines that the target user scores the plurality of candidate items, the candidate items whose scores are greater than the preset score threshold may be selected among the plurality of candidate items, and the determined candidate items may be determined as the target recommended items.

In a possible implementation manner, the scoring model is trained by acquiring attribute data of a plurality of users, attribute data of the plurality of items, and the scoring data; attribute data of the plurality of users, and attributes of the plurality of items. The data and the scoring data are processed to obtain a training data set, where the training data set includes an identifier of each user and a corresponding first interactive node list, an identifier of each item, and a corresponding second interactive node list, and each user pairs a score of one or more items in the item, the first interactive node list is used to represent the interaction information of the user with other users or items, and the second interactive node list is used to represent the interaction information of the item with other items or users; according to the training data set , training the scoring model.

In the solution shown in the embodiment of the present invention, in order to train the scoring model, the server may predetermine the training data set. Specifically, the server may acquire attribute data of the plurality of users, attribute data of the plurality of items, and scoring data, wherein the attribute data of each of the plurality of users may include an identifier of the corresponding user, and each of the plurality of items The attribute data may include an identification of the corresponding item, and the scoring data may include scoring of one or more of the plurality of items by each of the plurality of users. After obtaining the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data, the server may process the same to obtain a training data set, where the training data set may include the identifier and corresponding of each of the plurality of users. a first interactive node list, an identification of each of the plurality of items, and a corresponding second interactive node list, and each user scores one or more of the plurality of items. After obtaining the training data set, the server can train the above scoring model, that is, the model parameters in the scoring model can be adjusted to obtain the scoring model after training.

In a possible implementation manner, the attribute data of each of the multiple users further includes one or more of the following: gender, height, weight, age, occupation, income, hobbies, education, and more. The attribute data of each item in the item also includes one or more of the following data: brand, color, size, price, comment, taste, shelf life, icon; the scoring data also includes one or more of the following data: : Operating time, current equipment usage, discounts.

In a possible implementation manner, acquiring attribute data of multiple users, attribute data of multiple items, and scoring data includes: acquiring a plurality of scoring records, each of the plurality of scoring records including attribute data of the user u The attribute data of the item i and the score data of the item i by the user u, the user u is any one of the plurality of users who have scored the item i, and the item i is any one of the plurality of items; The attribute data of the user, the attribute data of the plurality of items, and the scoring data are processed to obtain a training data set, including: processing the plurality of scoring records to obtain a training data set, where each training data in the training data set includes the identifier of the user u and Corresponding first interactive node list, identifier of item i and corresponding second interactive node list, user u scores item i.

In the solution shown in the embodiment of the present invention, the server may acquire a plurality of scoring records, and each of the plurality of scoring records includes attribute data of the user u, attribute data of the item i, and scoring data of the item i by the user u, the item i is any one of a plurality of items, the user u is any one of a plurality of users who have overwhelmed the item i, the attribute data of the user u includes the identifier of the user u, and the attribute data of the item i includes the identifier of the item i The scoring data of the item i by the user u may include the scoring of the item i by the user u, wherein the scoring record may also be referred to as an interaction record (for example, if the user purchases an item, the scoring data in the corresponding scoring record may be 1) ). For example, the plurality of scoring records are (u ₀ , i ₀ , 1), (u ₀ , i ₁ , 1), (u ₀ , i ₂ , 1), respectively. After acquiring the plurality of scoring records, for each of the plurality of scoring records w, the training data g corresponding to the scoring record w can be obtained based on the scoring record w and the scoring record before the scoring record w. For example, the scoring record is first obtained as w ₀ (u ₀ , i ₀ , 1). Since the scoring record w ₀ is acquired for the first time, the first interactive node list corresponding to the user u ₀ is empty, and the item i ₀ corresponds to second interactive node list is empty, to obtain training data g ₀ w ₀ corresponding to the score recorded for the identification of a user u u _0, i, i ₀ tagged items, a first list of the corresponding user u interactive node is empty, the article i The corresponding second interactive node list is empty and is divided into 1; the second obtained scoring record is w ₁ (u ₀ , i ₁ , 1), so that it can be seen that the user u ₀ over-scoring the item i ₀ , the item i ₁ other users are not playing too, therefore, a first user u ₀ corresponding to the interactive node list is I _0, i ₁ corresponding to the second article interactive node list is empty, the corresponding scoring recording w ₁ g ₁ training data obtained for the user u ₀ u identification, identification article i i _1, a first interactive node list corresponding to the user u i _0, i corresponding to the second article interactive node list is empty, scored as 1; then acquired scoring record w ₂ (u ₁ , i ₁ , 1), it can be seen that user u _{1 has} not overrated other items. The item i ₁ is over-subscribed by the user u _0. Therefore, the first interactive node list corresponding to the user u ₁ is empty, the second interactive node list corresponding to the item i ₁ is u ₀ , and the obtained training data g corresponding to the scoring record w _{2 is} obtained. ₂ is the identifier u ₁ of the user u and the identifier i _{1 of the} item i. The first interactive node list corresponding to the user u is empty, and the second interactive node list corresponding to the item i is u ₀ and is divided into 1.

In a possible implementation manner, the scoring model includes a feature learning model, a feedback learning model, and a neural network model; wherein, according to the training data set, the scoring model is trained, including: inputting the identifier of the user u and the identifier of the item i The feature learning model obtains the feature vector corresponding to the user u and the feature vector corresponding to the item i, and inputs the first interactive node list corresponding to the user u and the second interactive node list corresponding to the item i into the feedback learning model to obtain the user u corresponding The implicit feedback corresponds to the implicit feedback corresponding to the item i; the feature vector corresponding to the user u and the feature vector corresponding to the item i, the implicit feedback corresponding to the user u, and the implicit feedback corresponding to the item i are input into the neural network model, Predicting the score; according to the predicted score and the user u's scoring of the item i, the feature learning model, the feedback learning model and the neural network model are adjusted to obtain the trained scoring model.

In the solution shown in the embodiment of the present invention, after obtaining the training data set, the server inputs the identifier of the user u and the identifier of the item i in each training data in the training data set into the feature learning model to obtain the feature vector and the item i corresponding to the user u. Corresponding feature vector, and the first interactive node list corresponding to the user u and the second interactive node list corresponding to the item i in each training data are input into the feedback learning model, and the implicit feedback corresponding to the user u and the item i corresponding are obtained. The implicit feedback, wherein the eigenvector corresponding to the user u and the eigenvector corresponding to the item i are similar to the eigenvector corresponding to the eigenvector corresponding to the target user and the eigenvector corresponding to the candidate item j, and the hidden corresponding to the user u is obtained. The specific manner of the implicit feedback corresponding to the item feedback and the item i is similar to the implicit feedback corresponding to the target user and the implicit feedback corresponding to the candidate item j, and details are not described herein. After obtaining the feature vector corresponding to the user u, the feature vector corresponding to the item i, the implicit feedback corresponding to the user u, and the implicit feedback corresponding to the item i, the server can input the neural network model to obtain the predicted score. After obtaining the predicted score of the user u on the item i, the model parameters of the feature learning model, the feedback learning model, and the neural network model may be adjusted according to the prediction score and the user u in each training data in the training data set. The trained scoring model is obtained, wherein the model parameters of the feature learning model, the feedback learning model and the neural network model can be adjusted based on the training principle that the predicted score approaches the scoring of the item i by the user u, and the trained model is obtained. Score the model.

In a possible implementation manner, the first interaction node list corresponding to the user u includes a multi-level first interaction node list, and the second interaction node list corresponding to the item i includes a multi-level second interaction node list, and the model of the feedback learning model is fed back. The parameter includes: a multi-level user feedback matrix and a multi-order item feedback matrix, wherein the order of the first interactive node list corresponding to the user u is the same as the order of the user feedback matrix, and the order of the second interactive node list corresponding to the item i The order of the first interactive node in the multi-order first interactive node list is used to represent the interaction information between the user and the item, and the first-order first interactive node list in the multi-level first interactive node list is used. In the multi-level second interaction node list, the odd-order second interaction node list is used to represent the interaction information between the item and the user, and the even-order second interaction node list in the multi-level second interaction node list is used to represent the interaction information between the user and other users. Used to indicate the interaction information of the item with other items; the first interactive node list corresponding to the user u, and the second corresponding to the item i The interaction node list inputs the feedback learning model, and obtains the implicit feedback corresponding to the user u and the implicit feedback corresponding to the item i, including: a multi-level first interaction node list corresponding to the user u, and a multi-level second interaction node corresponding to the item i The list input feedback learning model obtains implicit feedback corresponding to user u and implicit feedback corresponding to item i.

In the solution shown in the embodiment of the present invention, when training the scoring model, the server may further utilize a multi-level first interactive node list corresponding to the user u and a multi-level second interactive node list corresponding to the item i. For this situation, the server may input the multi-level first interaction node list corresponding to the user u and the multi-level second interaction node list corresponding to the item i into the feedback learning model, and obtain the implicit feedback corresponding to the user u and the hidden corresponding to the item i. Feedback.

In a possible implementation manner, the model parameters of the feedback learning model include: a weight of a feedback vector of each of the plurality of users, a weight of a feedback vector of each of the plurality of items; and a first corresponding to the user u The interaction node list and the second interaction node list corresponding to the item i are input to the feedback learning model, and the implicit feedback corresponding to the user u and the implicit feedback corresponding to the item i are obtained, including: the identifier of the user u and the corresponding first interaction node list The identifier of the item i and the corresponding second interactive node list are input into the feedback learning model, and the implicit feedback corresponding to the user u and the implicit feedback corresponding to the item i are obtained.

In the solution shown in the embodiment of the present invention, the server may input the identifier of the user u in each training data in the training data set and the corresponding first interactive node list, the identifier of the item i, and the corresponding second interactive node list, and input feedback learning. The model obtains implicit feedback corresponding to user u and implicit feedback corresponding to item i.

In a second aspect, a training method for a scoring model is provided, the method comprising: acquiring attribute data of a plurality of users, attribute data of a plurality of items, and scoring data; attribute data of a plurality of users, and attribute data of a plurality of items And scoring the data for processing, obtaining a training data set, where the training data set includes an identifier of each user and a corresponding first interactive node list, an identifier of each item, and a corresponding second interactive node list, and each user pairs multiple items The scoring of one or more items in the first interaction node list is used to represent the interaction information of the user with other users or items, and the second interaction node list is used to represent the interaction information of the item with other items or users; according to the training data set, Train the scoring model.

In a possible implementation manner, the first interaction node list corresponding to the user u includes a multi-level first interaction node list, and the second interaction node list corresponding to the item i includes a multi-level second interaction node list, and the model of the feedback learning model is fed back. The parameter includes: a multi-level user feedback matrix and a multi-order item feedback matrix, wherein the order of the first interactive node list corresponding to the user u is the same as the order of the user feedback matrix, and the order of the second interactive node list corresponding to the item i The order of the first interactive node in the multi-order first interactive node list is used to represent the interaction information between the user and the item, and the first-order first interactive node list in the multi-level first interactive node list is used. In the multi-level second interaction node list, the odd-order second interaction node list is used to represent the interaction information between the item and the user, and the even-order second interaction node list in the multi-level second interaction node list is used to represent the interaction information between the user and other users. Used to indicate the interaction information of the item with other items; the first interactive node list corresponding to the user u, and the second corresponding to the item i The mutual node list inputs the feedback learning model, and obtains the implicit feedback corresponding to the user u and the implicit feedback corresponding to the item i, including: a multi-level first interactive node list corresponding to the user u, and a multi-order second interactive node corresponding to the item i The list input feedback learning model obtains implicit feedback corresponding to user u and implicit feedback corresponding to item i.

In a third aspect, an apparatus for recommending an item is provided, the apparatus comprising at least one module for implementing the method of recommending an item provided by the first aspect above.

In a fourth aspect, an apparatus is provided, the apparatus comprising a processor, a memory and a transmitter, the processor being configured to execute instructions stored in the memory; the processor executing the instructions to cause the apparatus to implement the recommended item provided by the first aspect above Methods.

In a fifth aspect, a computer readable storage medium is provided, comprising instructions that, when executed on a computer, cause the computer to perform the method of the first aspect described above.

In a sixth aspect, a computer program product comprising instructions for causing the computer to perform the method of the first aspect described above when the computer program product is run on a computer.

In a seventh aspect, a training apparatus for a scoring model is provided, the apparatus comprising at least one module for implementing the training method of the scoring model provided by the second aspect above.

In an eighth aspect, an apparatus is provided, the apparatus comprising a processor, a memory and a transmitter, the processor being configured to execute instructions stored in the memory; the processor executing the instructions to cause the apparatus to implement the scoring model provided by the second aspect above Training method.

In a ninth aspect, a computer readable storage medium is provided, comprising instructions that, when executed on a computer, cause the computer to perform the method of the second aspect described above.

In a tenth aspect, a computer program product comprising instructions for causing the computer to perform the method of the second aspect described above when the computer program product is run on a computer.

The beneficial effects brought by the technical solutions provided by the embodiments of the present invention are:

In the embodiment of the present invention, the attribute data of the target user and the attribute data of the plurality of candidate items are acquired, and the attribute data of the target user includes the identifier of the target user, and the attribute data of each candidate item includes the identifier of the corresponding candidate item; The attribute data and the attribute data of the plurality of candidate items are processed to generate a target data set, where the target data set includes the identifier of the target user and the corresponding target first interactive node list, the identifier of each candidate item of the plurality of candidate items, and the corresponding a target second interaction node list, the target first interaction node list is used to represent interaction information of the target user with other users or items, and the target second interaction node list is used to represent interaction information of the candidate item with other items or users; The input scoring model is set to obtain the scoring of the plurality of candidate items by the target user, wherein the scoring model is obtained according to the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data, and the plurality of users include the target user and the plurality of users. The attribute data of each user in the user includes corresponding The identification of the household, the plurality of items comprising a plurality of candidate items, the attribute data of each of the plurality of items including the identification of the corresponding item, the scoring data comprising one or more items of each of the plurality of users The scoring of the target; the target recommended item is determined according to the scoring of the plurality of candidate items by the target user. In this way, the target user can select the desired item among the target recommended items recommended by the server, and does not need to select among all the items stored in the server, thereby improving the efficiency of the user selecting the item.

DRAWINGS

1 is a schematic diagram of a system framework provided by an embodiment of the present invention;

2 is a schematic structural diagram of a server according to an embodiment of the present invention;

3 is a schematic diagram of a bipartite diagram provided by an embodiment of the present invention;

4 is a flowchart of a method for recommending an item according to an embodiment of the present invention;

FIG. 5 is a flowchart of a training method for a scoring model according to an embodiment of the present invention; FIG.

6 is a schematic structural diagram of a device for recommending an article according to an embodiment of the present invention;

7 is a schematic structural diagram of an apparatus for recommending an article according to an embodiment of the present invention;

FIG. 8 is a schematic structural diagram of a training apparatus for a scoring model according to an embodiment of the present invention.

Detailed ways

An embodiment of the present invention provides a method for recommending an item. The execution subject of the method is a device, and the device may be a server. The server may be a background server that recommends the function of the item. The server may be a single server or a server group composed of multiple servers. The embodiment of the present invention uses a server as a separate server as an example for detailed description. Other situations are similar and will not be repeated. In order to improve the efficiency of the user to select an item, when the target user wants to select an item, the operation triggering terminal may send an item recommendation request corresponding to the target user to the server, and correspondingly, after receiving the item recommendation request, the server may be in the candidate set. Among the candidate items, the target recommended item that the target user may like is determined, and then the target recommended item may be sent to the terminal, and the terminal may display the target recommended item after receiving the target recommended item, so that the target user can select the desired item in the target recommended item. The required items, of which, the system frame diagram is shown in Figure 1.

The server may include a processor 210, a transmitter 220, a receiver 230, and a memory 240. The receiver 230 and the transmitter 220, and the memory 240 may be respectively coupled to the processor 210, as shown in FIG. The receiver 230 can be used to receive messages or data, the transmitter 220 and the receiver 230 can be network cards, and the transmitter 220 can be used to transmit messages or data, that is, the target recommended items can be sent to the target user's terminal. The processor 210 can be the control center of the server, connecting various parts of the entire server, such as the receiver 230, the transmitter 220, and the memory 240, using various interfaces and lines. In the present invention, the processor 210 may be a CPU (Central Processing Unit), which may be used to determine related processing of the target recommended item. Alternatively, the processor 210 may include one or more processing units; 210 may integrate an application processor and a modem processor, wherein the application processor primarily processes an operating system, and the modem processor primarily processes wireless communications. Processor 210 can also be a digital signal processor, an application specific integrated circuit, a field programmable gate array, or other programmable logic device or the like. The memory 240 can be used to store software programs and modules, and the processor 210 performs various functional applications and data processing of the server by reading software code and modules stored in the memory.

In order to facilitate the understanding of the embodiments of the present invention, the basic concepts involved in the embodiments of the present invention are first described below.

1, user-item two parts

That is, the user's interaction with the item can be represented by a bipartite graph, wherein the connected side between the user and the item indicates that the user's history has interacted with the item, and the two parts are described in detail below:

The server can obtain historical behavior data of each user (for example, in the case that the item is a movie, the historical behavior data of each user may be a movie that each user has downloaded, viewed, and collected), and further, for each user, the server According to the historical behavior data of the user, the user can establish the side of the item that the user interacts with, and thus obtain the user-item bipartite graph.

For example, the user-item two-part diagram is shown in FIG. 3, and user1 to user5 in FIG. 3 respectively represent five users, and item1 to item8 respectively represent eight items. Starting from a node in Figure 3, passing one edge is called a step. For the user, all nodes that can be reached in one step are items, and all nodes that can be reached in two steps are at least one of the same interactive items as the current user. All users, in which all nodes that can be reached in one step from the user may be referred to as a first-order first interactive node list, and all nodes that can be reached in two steps may be referred to as a second-order first interactive node list, and so on, starting from an item. All nodes that can be reached in one step may be referred to as a first-order second interactive node list, and all nodes that can be reached in two steps may be referred to as a second-order second interactive node list, and so on. The server may obtain a first interactive node list corresponding to each user and a second interactive node list corresponding to each item according to the user-item bipartite graph. For example, the first-order first interactive node list corresponding to user1 includes item1. The second-order first interactive node list of item2, item3, item8, and user1 includes user2, user3, and user5. For another example, the first-order second interaction node list corresponding to item1 includes user1 and user3, and the second-order second interaction node list corresponding to item1 includes item2, item3, item4, and item8.

The processing flow shown in FIG. 4 will be described in detail below with reference to specific implementations, and the content can be as follows:

Step 401: Acquire attribute data of the target user and attribute data of the plurality of candidate items. The attribute data of the target user includes an identifier of the target user, and the attribute data of each candidate item includes an identifier of the corresponding candidate item.

In the implementation, the item recommendation triggering event corresponding to each user may be pre-stored in the server, where the item recommendation triggering event corresponding to each user may be the same. For example, the item recommendation triggering event may be a preset item recommendation period, and each The item recommendation triggering event corresponding to the user may also be different. For example, the item recommendation triggering event corresponding to each user may be an item recommendation request sent by the user's terminal, respectively. When the server detects that the item recommendation triggering event of the corresponding target user occurs (for example, when detecting the item recommendation request of the corresponding target user sent by the terminal), the server may determine the target recommended item that the target user likes and recommend it to Target users. Specifically, the server may acquire attribute data of the target user and attribute data of each candidate item of the plurality of candidate items, wherein the attribute data of the target user may include an identifier of the target user, and attributes of each candidate item of the plurality of candidate items The data may include an identification of the corresponding candidate item.

Optionally, the attribute data of the target user may further include one or more of the following data: gender, height, weight, age, occupation, income, hobbies, education, and attribute data of each candidate item may further include the following: One or more of the data: brand, color, size, price, comment, taste, shelf life, icon.

Step 402: Process attribute data of the target user and attribute data of the plurality of candidate items to generate a target data set, where the target data set includes an identifier of the target user and a corresponding target first interaction node list, and each of the plurality of candidate items. The identifier of the candidate item and the corresponding target second interaction node list, the target first interaction node list is used to represent the interaction information of the target user with other users or items, and the target second interaction node list is used to represent the candidate item and other items or users. Interaction information.

In an implementation, after the server obtains the attribute data of the target user and the attribute data of the plurality of candidate items, the server may process the target data set, where the target data set may include the target user's identifier and the corresponding target first. An interaction node list, an identifier of each candidate item in the plurality of candidate items, and a corresponding target second interaction node list, wherein the target first interaction node list is used to represent interaction information between the target user and other users or items, and the target second interaction node The list is used to represent the interaction information of the candidate item with other items or users.

Optionally, after obtaining the identifier of the target user and the identifier of each candidate item, determining, according to the identifier of the target user and the identifier of each candidate item, the target first interaction node list and the target second interaction corresponding to each candidate item. The node list, correspondingly, the process of step 402 may be as follows: according to the identifier of the target user, determine the target corresponding to the target user in the target first interaction node list corresponding to the identifier of each user among the plurality of pre-recorded users. An interactive node list, and determining, according to the identifier of each candidate item, a target second interaction corresponding to each candidate item in a target second interaction node list corresponding to the identifier of each candidate item among the plurality of candidate items recorded in advance a node list; generating a target data set according to the identifier of the target user, the target first interaction node list corresponding to the target user, the identifier of each candidate item, and the target second interaction node list corresponding to each candidate item.

In the implementation, the server may pre-store a target first interaction node list corresponding to the identifier of each user of the plurality of users, and a target second interaction node list corresponding to the identifier of each candidate item of the plurality of candidate items, where The server may record, in the form of a table, a target first interaction node list corresponding to the identifier of each user and a target second interaction node list corresponding to the identifier of each candidate item, and may also record the identifier of each user in the form of a bipartite graph. Corresponding target first interactive node list and a target second interactive node list corresponding to the identifier of each candidate item. After the server obtains the identifier of the target user and the identifier of each candidate item, the target first interaction node corresponding to the target user may be determined in the target first interaction node list corresponding to the identifier of each user among the plurality of pre-recorded users. a list, and the target second interaction node list corresponding to each candidate item may be determined in the target second interaction node list corresponding to the identifier of each candidate item among the plurality of candidate items recorded in advance. After determining the target first interaction node list corresponding to the target user and the target second interaction node list corresponding to each candidate item, the server may generate the identifier including the target user and the corresponding target first interaction node list, and each candidate item. Identifying and corresponding target data sets of the target second interaction node list, wherein each target data in the target data set may include the identifier of the target user and the corresponding target first interaction node list, the identifier of the candidate item j, and the corresponding target number The second interactive node list, the candidate item j is any one of the plurality of candidate items.

Step 403, the target data set is input into the scoring model, and the target user scores the plurality of candidate items, wherein the scoring model is obtained according to the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data training. The attribute data of each user of the users includes the identifier of the corresponding user, and the attribute data of each item of the plurality of items includes the identifier of the corresponding item, and the scoring data includes one of the plurality of users for each of the plurality of items or Score multiple items.

In the implementation, the scoring model may be pre-stored in the server, wherein the scoring model may be obtained by the server according to attribute data of multiple users, attribute data of multiple items, and scoring data, and multiple users include target users and multiple The item includes a plurality of candidate items. The server may predict the target user's scoring of each of the plurality of candidate items by the scoring model. Specifically, after generating the target data set, the server may input the target data set into the scoring model to obtain a scoring of each candidate item among the plurality of candidate items by the target user, where the server includes multiple target data for the target data set, the server Each target data can be input into the scoring model to obtain a score of the corresponding candidate item by the target user.

Optionally, the scoring model may include a feature learning model, a feedback learning model, and a neural network model. Correspondingly, the processing of step 403 may be as follows: inputting the identifier of the target user in the target data set and the identifier of the candidate item j into the feature learning model. Obtaining a feature vector corresponding to the target user and a feature vector corresponding to the candidate item j, and inputting the target first interactive node list corresponding to the target user in the target data set and the target second interactive node list corresponding to the candidate item j, and inputting the feedback learning model Obtaining implicit feedback corresponding to the target user and implicit feedback corresponding to the candidate item j, wherein the item j is any one of the plurality of candidate items; and the feature vector corresponding to the target user and the feature vector corresponding to the candidate item j The implicit feedback corresponding to the target user and the implicit feedback corresponding to the candidate item j are input into the neural network model, and the target user is scored for the candidate item j.

In an implementation, the scoring model may include a feature learning model, a feedback learning model, and a neural network model, wherein the feature learning model may be used to learn a target user and a feature vector corresponding to each candidate item, and the model parameters of the feature learning model may include the user. The feature matrix and the item feature matrix, wherein the user feature matrix is composed of feature vectors of each user of the plurality of users (ie, each row of the user feature matrix is a feature vector corresponding to the user, and the number of rows of the user feature matrix is The number of the plurality of users), the item feature matrix is composed of the feature vectors of each of the plurality of items (ie, each line of the item feature matrix is a feature vector of the corresponding item, and the number of lines of the item feature matrix is multiple The number of items). After obtaining the target data set, the server may input the identifier of the target user and the identifier of the candidate item j into the feature learning model to obtain the feature vector corresponding to the target user and the feature vector corresponding to the candidate item j. Specifically, after the server inputs the identifier of the target user and the identifier of the candidate item j into the feature learning model, the feature vector corresponding to the target user is extracted in the user feature matrix by the feature learning model according to the identifier of the target user and the identifier of the candidate item j. The feature vector corresponding to the candidate item j is extracted in the item feature matrix, and the feature vector corresponding to the target user and the feature vector corresponding to the candidate item j are obtained.

The feedback learning model can be used to learn implicit feedback corresponding to the target user and each candidate item, and the model parameters of the feedback learning model can include a user feedback matrix (which can be represented by Y) and an item feedback matrix (which can be represented by X), wherein The user feedback matrix may be composed of feedback vectors (each row vector in the user feedback matrix represents a feedback vector corresponding to one node), and the item feedback matrix may be composed of feedback vectors (each row vector in the item feedback matrix represents one node) Corresponding feedback vector). After determining the target first interactive node list (which may be represented by R) corresponding to the target user (which may be represented by _k ), and selecting the target second interactive node list corresponding to the candidate item j (which may be represented by R _j ), The feedback learning model is input, and the implicit feedback corresponding to the target user k and the implicit feedback corresponding to the candidate item j are obtained. Specifically, after the server inputs the target first interaction node list corresponding to the target user k into the feedback learning model, the feedback learning model may extract multiple feedback vectors corresponding to the target first interaction node list in the user feedback matrix (wherein the feedback vector) The number of nodes is the number of nodes included in the target first interactive node list), and the feedback vector corresponding to the target user k is obtained. After obtaining the feedback vector corresponding to the target user k, multiple feedback vectors may be added to obtain implicit feedback corresponding to the target user k. The server may obtain the implicit feedback P corresponding to the target user k according to formula (1). _k ,

Wherein, each row vector in the user feedback matrix can be represented by Y _t , where t is the identifier of the node, t=1, 2, . . . , M, M is the total number of rows of the user feedback matrix. It should be noted that when the user feedback matrix is an odd-order user feedback matrix, M is the total number of multiple items. When the user feedback matrix is an even-order user feedback matrix, M is the total number of multiple users.

The specific processing of obtaining the implicit feedback corresponding to the candidate item j may be as follows: after the server inputs the target second interactive node list corresponding to the candidate item j into the feedback learning model, the target second interaction node may be extracted in the item feedback matrix by using the feedback learning model. A plurality of feedback vectors corresponding to the list (wherein the number of feedback vectors is the number of nodes included in the target second interactive node list), and a feedback vector corresponding to the candidate item j is obtained. After obtaining the feedback vector corresponding to the candidate item j, multiple feedback vectors may be added to obtain implicit feedback corresponding to the candidate item j, wherein the server may obtain the implicit feedback Q corresponding to the candidate item j according to formula (2). _j ,

Wherein, each row vector in the item feedback matrix can be represented by X _v , where v is the identifier of the node, and v=1, 2, . . . , N, N are the total number of rows of the item feedback matrix. It should be noted that when the item feedback matrix is an odd-order item feedback matrix, N is the total number of multiple users, and when the item feedback matrix is an even-order item feedback matrix, N is the total number of items.

Specifically, the server may pre-store a pre-trained neural network model, wherein the neural network model may include a multi-layer neural network, and the input of each layer of the neural network in the multi-layer neural network may be the upper layer neural network. Output, wherein the formula of the h-th neural network can be as shown in formula (3),

r ^h+1 =σ(W ^h r ^h +b ^h ) (3)

Where σ() is called an activation function, such as sigmoid function, relu function, tanh function, etc., r ^h is the input of layer h, b ^h is the offset item of layer h, and W ^h is the layer h nerve The weights on the side edges of the neurons and the h+1th layer neurons, wherein W ^h and b ^h are also trained, and the input r ^{1 of the} first layer neural network can be as shown in formula (4).

r ¹ =<p+P,q+Q> (4)

Where <x,y> denotes that the vector x is multiplied by the value of the dimension corresponding to the vector y, r ¹ is a vector, p represents the feature vector corresponding to the user, P represents the implicit feedback corresponding to the user, and q represents the feature vector corresponding to the item. Q indicates the implicit feedback corresponding to the item. Therefore, the neural network model can be as shown in equation (5), where H is the total number of layers of the neural network model.

y=σ(W ^H (σ(W ^H-1 (σ(...σ(W ¹ r ¹ +b ¹ )+...+b ^H-1 ))+b ^H ) (5)

The server determines the target user k corresponding eigenvectors P _k, implicit feedback target user corresponding to k P _k, the feature vector Q _j, potential item j corresponding implicit feedback Q _j candidate item j corresponding, you may be r ¹ _Kj is input to the neural network model and is brought to the formula (5) to obtain the score of the candidate item j by the target user k. Where r ¹ _{kj is} as shown in equation (6).

In addition, the _{sum of} b _k , b _j and b may be included in the formula (6) (the _{sum of} b _k , b _j and b may be referred to as a statistical reference score), where b is all the scores included in the training data set. The mean value, b _k is the difference between the mean value of all the scores of the target users k for each item included in the training data set and b, and b _j is the difference between the mean value of the scores of all the users included in the training data set and the b. .

Optionally, the target first interaction node list may include a multi-level target first interaction node list, and the target second interaction node list corresponding to each candidate item may include a multi-level target second interaction node list, and the multi-level target first interaction node The first interactive node list in the node list is used to represent the interaction information between the target user and the item, and the even-order target in the multi-level target first interactive node list is used to represent the interaction information between the target user and other users. The second-order target second interaction node list in the multi-level target second interaction node list is used to represent the interaction information between the candidate item and the user, and the even-order target second interaction node list in the multi-level target second interaction node list is used to represent the candidate The interaction information of the item and other items; the model parameters of the feedback learning model may include a multi-level user feedback matrix and a multi-order item feedback matrix, wherein the order of the target first interactive node list is the same as the order of the user feedback matrix, and each The order of the target second interactive node list corresponding to the candidate item and the item feedback moment The order of the array is the same. For this situation, correspondingly, the specific processing process of obtaining the implicit feedback corresponding to the target user and the implicit feedback corresponding to the candidate item j may be as follows: a multi-level target first interactive node list corresponding to the target user in the target data set and The candidate object j corresponds to the multi-level target second interaction node list, and inputs the feedback learning model to obtain implicit feedback corresponding to the target user and implicit feedback corresponding to the candidate item j.

In an implementation, when the server predicts the target user to score the candidate item j, the server may also use the multi-level target first interaction node list corresponding to the target user, and the multi-level target second interaction node list corresponding to the candidate item j, where The first-level target interaction node list may be a first-order target first interaction node list, a second-order target first interaction node list, ..., an A-order target first interaction node list, and the multi-level user feedback matrix may include first-order user feedback. Matrix, second-order user feedback matrix, ..., A-order user feedback matrix, A is a preset value (for example, A is 3), and A is the maximum number of steps that the target user can reach in the user-item map. The multi-level target second interaction node list may be a first-order target second interaction node list, a second-order target second interaction node list, ..., a B-order target second interaction node list, and the multi-level item feedback matrix may include a first-order item. Feedback matrix, second-order item feedback matrix, ..., B-order item feedback matrix, B is the preset value, B is the default candidate item j can be found in the user-item two-part map The maximum number of steps, wherein, A and B may be the same or different. The first-order user feedback matrix can be Y ¹ , and each row vector in the first-order user feedback matrix can be a vector representation of the corresponding item as a node in the first-order target first interaction node list, and a second-order user feedback matrix can Expressed by Y ² , each row vector in the second-order user feedback matrix may be a vector representation when the corresponding user is a node in the second-order target first interaction node list, and so on. The first-order item feedback matrix can be X ¹ , and each row vector in the first-order item feedback matrix can be a vector representation of the corresponding user as a node in the first-order target second interactive node list, and a second-order item feedback matrix can Expressed by X ² , each row vector in the second-order item feedback matrix may be a vector representation of the corresponding item as a node in the second-order target second interactive node list, and so on.

Corresponding feedback vector. The server may select the feedback vector corresponding to the first interaction node list of each target object corresponding to the target user according to the above manner, and then add all the selected feedback vectors to obtain the implicit feedback corresponding to the target user.

List of second-order interaction nodes per target for the candidate item j

Corresponding feedback vector. The server may select the feedback vector corresponding to the second interactive node list of each target object corresponding to the candidate item j according to the above manner, and further, all the selected feedback vectors may be added to obtain the implicit feedback corresponding to the candidate item j.

Optionally, the model parameter of the feedback learning model may further include: a weight of a feedback vector of each of the plurality of users, a weight of a feedback vector of each of the plurality of items, wherein the weight may be pre-trained by the server. In this case, the specific process of obtaining the implicit feedback corresponding to the target user and the implicit feedback corresponding to the candidate item j may be as follows: the identifier of the target user in the target data set and the corresponding target first interaction node list, The identifier of the candidate item j and the corresponding target second interaction node list are input, and the feedback learning model is input, and the implicit feedback corresponding to the target user and the implicit feedback corresponding to the candidate item j are obtained.

In an implementation, the model parameter for the feedback learning model further includes a weight of a feedback vector of each of the plurality of users and a weight of a feedback vector of each of the plurality of items, and the server may target the target user in the target data set The identifier and the corresponding target first interaction node list, the identifier of the candidate item j, and the corresponding target second interaction node list, input a feedback learning model, and obtain implicit feedback corresponding to the target user and implicit feedback corresponding to the candidate item j. Specifically, the server may determine the feedback vector corresponding to the target user and the feedback vector corresponding to the candidate item j by using the feedback learning model according to the method for determining the feedback vector corresponding to the target user and the candidate item j. Then, the server may weight and process the feedback vector corresponding to the target user k by feeding back the weight of the feedback vector of the target user in the learning model (which may be represented by Φ _kt ) according to the identifier of the target user and the identifier of the candidate item j. The implicit feedback corresponding to the target user, and by weighting the feedback vector of the candidate item j in the feedback learning model (which can be represented by Ω _vj ), the feedback vector corresponding to the candidate item j is weighted and processed to obtain the hidden corresponding to the candidate item j. Feedback.

Optionally, the model parameters of the feedback learning model may include: weights of feedback vectors of each of the plurality of users, weights of feedback vectors of each of the plurality of items, multi-level user feedback matrix, and multi-level item feedback a matrix, correspondingly, the target first interaction node list corresponding to the target user may include a multi-level target first interaction node list, and the target second interaction node list corresponding to each candidate item may include a multi-level target second interaction node list, corresponding The process of determining the implicit feedback corresponding to the target user and the implicit feedback corresponding to the candidate item j may be as follows: the identifier of the target user in the target data set and the corresponding multi-level target first interactive node list, candidate item j The identifier and the corresponding multi-level target second interaction node list are input, and the feedback learning model is input, and the implicit feedback corresponding to the target user and the implicit feedback corresponding to the candidate item j are obtained.

In an implementation manner, the server may select, according to the foregoing manner, a feedback vector corresponding to the first interaction node list of each target object corresponding to the target user and a feedback vector corresponding to each second-order target second interaction node list corresponding to the candidate item j. Then, the server may feedback the weight of the feedback vector of the target user in the learning model according to the identifier of the target user and the identifier of the candidate item j (may be used)

Representing) weighting and processing the feedback vector corresponding to the target user k, obtaining implicit feedback corresponding to the target user, and feeding back the weight of the feedback vector of the candidate item j in the learning model (can be used)

It is shown that the feedback vector corresponding to the candidate item j is weighted and processed to obtain implicit feedback corresponding to the candidate item j.

Specifically, the server can obtain the implicit feedback P _k corresponding to the target user according to formula (7).

among them,

Represents the feedback vector of the target user k

the weight of.

The server can obtain the implicit feedback Q _j corresponding to the candidate item j according to formula (8).

among them,

a feedback vector representing the candidate item j

the weight of.

Step 404: Determine a target recommended item according to the target user's scoring of the plurality of candidate items.

In an implementation, after obtaining the scoring of the plurality of candidate items by the target user, the server may determine, in the plurality of candidate items, the target recommended items to be recommended to the target user according to the scoring of each candidate item of the plurality of candidate items by the target user. Further, the target recommended item can be recommended to the target user.

Optionally, the preset recommendation condition may be stored in the server. Correspondingly, the processing of step 404 may be as follows: determining, according to the target user's scoring of the plurality of candidate items, the corresponding recommended item that meets the preset recommendation condition.

The preset recommendation condition may be a condition for the server to determine whether an item is recommended according to the corresponding score.

In the implementation, the server may pre-store the preset recommendation condition, and after the server obtains the score of the plurality of candidate items by the target user, the target recommended item that meets the preset recommendation condition may be selected from the plurality of candidate items.

Optionally, based on different preset recommendation conditions, determining the processing method of the target recommended item may be various, and several feasible processing methods are given below:

In the first manner, according to the score of the plurality of candidate items by the target user, a preset number of target recommended items with the largest scores are determined.

In the implementation, after the server determines that the target user scores the plurality of candidate items, the plurality of candidate items may be sorted according to the order of the corresponding scores, and then the preset number of candidates are ranked first. Item, determined as the target recommended item.

In the second manner, according to the target user's scoring of the plurality of candidate items, the target recommended item whose score is greater than the preset score threshold is determined.

In an implementation, a preset score threshold may be pre-stored in the server. After the server determines that the target user scores the plurality of candidate items, the candidate items whose scores are greater than the preset score threshold may be selected among the plurality of candidate items, and the determined candidate items may be determined as the target recommended items.

The embodiment of the present invention further provides a training method for the scoring model. The processing flow shown in FIG. 5 will be described in detail below in conjunction with the specific implementation manner, and the content may be as follows:

Step 501: Acquire attribute data of a plurality of users, attribute data of a plurality of items, and scoring data.

In an implementation, to train the scoring model, the server may pre-determine the training data set. Specifically, the server may acquire attribute data of the plurality of users, attribute data of the plurality of items, and scoring data, wherein the attribute data of each of the plurality of users may include an identifier of the corresponding user, and each of the plurality of items The attribute data may include an identification of the corresponding item, and the scoring data may include scoring of one or more of the plurality of items by each of the plurality of users.

Optionally, the attribute data of each of the plurality of users may further include one or more of the following: gender, height, weight, age, occupation, income, hobbies, education, and each of the plurality of items. The attribute data of an item may further include one or more of the following data: brand, color, size, price, comment, taste, shelf life, icon; the scoring data may also include one or more of the following data: operation Time, current equipment usage, discounts.

Optionally, the server may obtain attribute data of multiple users, attribute data of multiple items, and scoring data by acquiring multiple scoring records. Correspondingly, the process of step 501 may be as follows: acquiring multiple scoring records, and more Each scoring record in the scoring record includes attribute data of the user u, attribute data of the item i, and scoring data of the item i by the user u, and the user u is any one of the plurality of users who have beaten the item i, the item i is any of a plurality of items.

In an implementation, the server may acquire a plurality of scoring records, wherein each of the plurality of scoring records includes attribute data of the user u, attribute data of the item i, and scoring data of the item i by the user u, and the item i is a plurality of items. Any one of the items, the user u is any one of a plurality of users who have overwhelmed the item i, the attribute data of the user u includes the identifier of the user u, the attribute data of the item i includes the identifier of the item i, and the user u pairs the item The scoring data of i may include the scoring of the item i by the user u, wherein the scoring record may also be referred to as an interactive record (for example, if the user has purchased an item, the scoring data in the corresponding scoring record may be 1). For example, the plurality of scoring records are (u ₀ , i ₀ , 1), (u ₀ , i ₁ , 1), (u ₀ , i ₂ , 1), respectively.

Step 502, processing attribute data of multiple users, attribute data of multiple items, and scoring data to obtain a training data set, where the training data set includes an identifier of each user and a corresponding first interactive node list, and each item Identifying and corresponding a second interactive node list, each user scoring one or more items of the plurality of items, the first interactive node list is used to represent interaction information of the user with other users or items, and the second interactive node list is used by Represents information about the interaction of an item with other items or users.

In an implementation, after obtaining attribute data of multiple users, attribute data of multiple items, and scoring data, the server may process the same, and obtain a training data set, where the training data set may include each user of multiple users. And the corresponding first interactive node list, the identifier of each item of the plurality of items, and the corresponding second interactive node list, each user scoring one or more items of the plurality of items.

Optionally, for the case of acquiring multiple scoring records, correspondingly, the process of step 502 may be as follows: processing a plurality of scoring records to obtain a training data set, where each training data in the training data set includes the identifier of the user u and Corresponding first interactive node list, identifier of item i and corresponding second interactive node list, user u scores item i.

In the implementation, after obtaining the plurality of scoring records, for each of the plurality of scoring records w, the training data corresponding to the scoring record w may be obtained according to the scoring record w and the scoring record before the scoring record w. . For example, the scoring record first acquired is w ₀ (u ₀ , i ₀ , 1). Since the scoring record w ₀ is acquired for the first time, the first interactive node list corresponding to the user u ₀ is empty, and the item i ₀ the corresponding node list is empty second interaction, to obtain training data g ₀ w ₀ corresponding to the score recorded for the identification of a user u u _0, i, i ₀ tagged items, a first list of the corresponding user u interactive node is empty, the article The second interactive node list corresponding to i is empty and is divided into 1; the second obtained scoring record is w ₁ (u ₀ , i ₁ , 1), so that it can be seen that the user u ₀ over-scoring the item i ₀ , the item i ₁ is not too much play other users, and therefore, a first user u ₀ corresponding to the interactive node list is I _0, the second list of items I ₁ corresponding to the interactive node is empty, the resulting score recording w ₁ g ₁ corresponds to the training data The identifier u _{0 of the} user u, the identifier i _{1 of the} item i, the first interactive node list corresponding to the user u is i ₀ , the second interactive node list corresponding to the item i is empty, and is scored 1; then the obtained scoring record is w ₂ (u ₁ , i ₁ , 1), it can be seen that user u _{1 has} not played against other items The item i ₁ is over-subscribed by the user u ₀ . Therefore, the first interactive node list corresponding to the user u ₁ is empty, the second interactive node list corresponding to the item i ₁ is u ₀ , and the obtained scoring record w ₂ corresponds to the training. The data g ₂ is the identifier u ₁ of the user u and the identifier i _{1 of the} item i. The first interactive node list corresponding to the user u is empty, and the second interactive node list corresponding to the item i is u ₀ and is divided into 1.

In step 503, the scoring model is trained according to the training data set.

In the implementation, after obtaining the training data set, the server may train the scoring model, that is, the model parameters in the scoring model may be adjusted to obtain the scoring model after training.

Optionally, for the case that the scoring model includes a feature learning model, a feedback learning model, and a neural network model, the server may uniformly train the feature learning model, the feedback learning model, and the neural network model. Accordingly, the processing of step 503 may be as follows: The identifier of the user u and the identifier of the item i are input into the feature learning model, and the feature vector corresponding to the user u and the feature vector corresponding to the item i are obtained, and the first interaction node list corresponding to the user u and the second interaction corresponding to the item i are obtained. The node list input feedback learning model, and the implicit feedback corresponding to the user u and the implicit feedback corresponding to the item i are obtained; the feature vector corresponding to the user u and the feature vector corresponding to the item i, and the implicit feedback corresponding to the user u correspond to the item i The implicit feedback input neural network model obtains the predicted score; according to the predicted score and the user u scores the item i, the feature learning model, the feedback learning model and the neural network model are adjusted to obtain the trained scoring model.

In the implementation, after obtaining the training data set, the server inputs the identifier of the user u and the identifier of the item i in each training data in the training data set into the feature learning model, and obtains the feature vector corresponding to the user u and the feature vector corresponding to the item i, And the first interaction node list corresponding to the user u and the second interaction node list corresponding to the item i in each training data are input into the feedback learning model, and the implicit feedback corresponding to the user u and the implicit feedback corresponding to the item i are obtained. The specific manner of obtaining the feature vector corresponding to the user u and the feature vector corresponding to the item i is similar to the method of obtaining the feature vector corresponding to the target user and the feature vector corresponding to the candidate item j, and obtaining the implicit feedback and the item i corresponding to the user u. The specific manner of the corresponding implicit feedback is similar to the implicit feedback corresponding to the target user and the implicit feedback corresponding to the candidate item j, and details are not described herein. The feature vector corresponding to the user u, the feature vector corresponding to the item i, the implicit feedback corresponding to the user u, and the implicit feedback corresponding to the item i are obtained, and then input into the neural network model to obtain a predicted score. After obtaining the predicted score of the user u on the item i, the model parameters of the feature learning model, the feedback learning model, and the neural network model may be adjusted according to the prediction score and the user u in each training data in the training data set. The trained scoring model is obtained, wherein the model parameters of the feature learning model, the feedback learning model and the neural network model can be adjusted based on the training principle that the predicted score approaches the scoring of the item i by the user u, and the trained model is obtained. Score the model.

Optionally, the first interaction node list corresponding to the user u may include a multi-level first interaction node list, and the second interaction node list corresponding to the item i may include a multi-level second interaction node list, and the model parameters of the feedback learning model may include a multi-level user feedback matrix and a multi-order item feedback matrix, wherein the order of the first interactive node list corresponding to the user u is the same as the order of the user feedback matrix, and the order and the item of the second interactive node list corresponding to the item i The order of the feedback matrix is the same, and the first interactive node list of the odd-order first interactive node list is used to represent the interaction information between the user and the item, and the even-numbered first interactive node list in the multi-level first interactive node list is used to represent User interaction information with other users, the odd-order second interaction node list in the multi-level second interaction node list is used to represent the interaction information between the item and the user, and the even-order second interaction node list in the multi-level second interaction node list is used for Represents information about the interaction of an item with other items. For this case, correspondingly, the specific processing of determining the implicit feedback corresponding to the user u and the implicit feedback corresponding to the item i may be as follows: a multi-order first interactive node list corresponding to the user u, and a multi-order corresponding to the item i The second interactive node list inputs the feedback learning model, and obtains implicit feedback corresponding to the user u and implicit feedback corresponding to the item i.

In the implementation, when training the scoring model, the server may also utilize the multi-level first interactive node list corresponding to the user u and the multi-level second interactive node list corresponding to the item i. For this situation, the server may input the multi-level first interaction node list corresponding to the user u and the multi-level second interaction node list corresponding to the item i into the feedback learning model, and obtain the implicit feedback corresponding to the user u and the hidden corresponding to the item i. Feedback.

Optionally, the model parameters of the feedback learning model may include: a weight of a feedback vector of each of the plurality of users, and a weight of a feedback vector of each of the plurality of items. In this case, the specific processing of determining the implicit feedback corresponding to the user u and the implicit feedback corresponding to the item i may be as follows: the identifier of the user u and the corresponding first interactive node list, the identifier of the item i, and the corresponding The second interactive node list inputs the feedback learning model, and obtains the implicit feedback corresponding to the user u and the implicit feedback corresponding to the item i.

In an implementation, the server may input the identifier of the user u in each training data in the training data set and the corresponding first interactive node list, the identifier of the item i, and the corresponding second interactive node list, and input the feedback learning model to obtain the user u. Corresponding implicit feedback and implicit feedback corresponding to item i.

Based on the same technical concept, an embodiment of the present invention further provides a device for recommending an item. As shown in FIG. 6, the device includes:

The obtaining module 610 is configured to acquire attribute data of the target user and attribute data of the plurality of candidate items, where the attribute data of the target user includes an identifier of the target user, and the attribute data of each candidate item includes an identifier of the corresponding candidate item, specifically The acquisition function in the above step 401 is implemented, as well as other implicit steps.

a generating module 620, configured to process attribute data of the target user and attribute data of the plurality of candidate items to generate a target data set, where the target data set includes an identifier of the target user and a corresponding target first An interaction node list, an identifier of each candidate item in the plurality of candidate items, and a corresponding target second interaction node list, wherein the target first interaction node list is used to represent interaction information between the target user and other users or items The target second interaction node list is used to represent the interaction information of the candidate item with other items or users, and specifically, the generation function in the above step 402, and other implicit steps may be implemented.

a scoring module 630, configured to input the target data set into a scoring model, and obtain a score of the plurality of candidate items by the target user, wherein the scoring model is based on attribute data of multiple users, attributes of multiple items Data and training of the scoring data, wherein the attribute data of each of the plurality of users includes an identifier of the corresponding user, and the attribute data of each of the plurality of items includes an identifier of the corresponding item, and the scoring data Including the scoring of one or more of the plurality of items by each of the plurality of users, specifically performing the scoring function in the above step 403, and other implicit steps.

The determining module 640 is configured to determine the target recommended item according to the target user's scoring of the plurality of candidate items, and specifically may implement the determining function in the foregoing step 404, and other implicit steps.

Optionally, the attribute data of the target user further includes one or more of the following data: gender, height, weight, age, occupation, income, hobbies, education, and attribute data of each candidate item includes the following: One or more of the data: brand, color, size, price, comment, taste, shelf life, icon.

Optionally, the generating module 620 is configured to:

Determining, according to the identifier of the target user, a target first interaction node list corresponding to the target user in a target first interaction node list corresponding to the identifier of each user of the plurality of pre-recorded users, and according to each candidate Determining, in the target second interaction node list corresponding to the identifier of each candidate item among the plurality of candidate items recorded in advance, determining a target second interaction node list corresponding to each candidate item;

And generating a target data set according to the identifier of the target user, the target first interaction node list corresponding to the target user, the identifier of each candidate item, and the target second interaction node list corresponding to each candidate item.

Optionally, the scoring model includes a feature learning model, a feedback learning model, and a neural network model;

The scoring module 630 is configured to:

Inputting the identifier of the target user and the identifier of the candidate item j in the target data set into the feature learning model, obtaining a feature vector corresponding to the target user and a feature vector corresponding to the candidate item j, and using the target data Concentrating the target first interaction node list corresponding to the target user and the target second interaction node list corresponding to the candidate item j, inputting the feedback learning model, obtaining implicit feedback corresponding to the target user, and the candidate An implicit feedback corresponding to the item j, wherein the item j is any one of the plurality of candidate items;

And inputting a feature vector corresponding to the target user, a feature vector corresponding to the candidate item j, an implicit feedback corresponding to the target user, and an implicit feedback corresponding to the candidate item j into a neural network model to obtain the target The user scores the candidate item j.

Optionally, the target first interaction node list includes a multi-level target first interaction node list, and the target second interaction node list corresponding to each candidate item includes a multi-level target second interaction node list, and the multi-level target first interaction node The first interactive node list in the node list is used to represent the interaction information between the target user and the item, and the even-order target in the multi-level target first interactive node list is used to represent the interaction information between the target user and other users. The second-order target second interaction node list in the multi-level target second interaction node list is used to represent the interaction information between the candidate item and the user, and the even-order target second interaction node list in the multi-level target second interaction node list is used to represent the candidate Information on the interaction of items with other items;

The scoring module 630 is configured to:

And inputting the feedback learning model to the multi-level target first interaction node list corresponding to the target user and the multi-level target second interaction node list corresponding to the candidate item j in the target data set, to obtain the corresponding target user Implicit feedback and implicit feedback corresponding to candidate j.

Optionally, the model parameter of the feedback learning model includes: a weight of a feedback vector of each of the plurality of users, and a weight of a feedback vector of each of the plurality of items;

The scoring module 630 is configured to:

And inputting the identifier of the target user in the target data set and the corresponding target first interaction node list, the identifier of the candidate item j, and the corresponding target second interaction node list into the feedback learning model to obtain the target user. The corresponding implicit feedback and the implicit feedback corresponding to the candidate item j.

Optionally, the determining module 640 is configured to:

And determining, according to the scoring of the plurality of candidate items by the target user, the corresponding recommended item that meets the preset recommendation condition.

Optionally, the determining module 640 is configured to:

Determining, according to the scoring of the plurality of candidate items by the target user, a preset number of target recommended items with a maximum score; or

And determining, according to the scoring of the plurality of candidate items by the target user, a target recommended item whose corresponding score is greater than a preset score threshold.

Optionally, as shown in FIG. 7, the acquiring module 610 is further configured to:

Obtaining attribute data of the plurality of users, attribute data of the plurality of items, and the scoring data;

The generating module 620 is further configured to:

Processing the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data to obtain a training data set, where the training data set includes an identifier of each user and a corresponding first interactive node list And an identifier of each item and a corresponding second interactive node list, each user scoring one or more items of the plurality of items, the first interactive node list being used to represent the user and other users or items Interactive information, the second interactive node list is used to indicate interaction information of the item with other items or users;

The device also includes:

The training module 650 is configured to train the scoring model according to the training data set.

Optionally, the attribute data of each of the multiple users further includes one or more of the following: gender, height, weight, age, occupation, income, hobbies, education, and the plurality of The attribute data of each item in the item further includes one or more of the following data: brand, color, size, price, comment, taste, shelf life, icon; the scoring data further includes one or more of the following data Kind: operating time, current equipment, discounts.

Optionally, the obtaining module 610 is configured to:

Acquiring a plurality of scoring records, each of the plurality of scoring records including attribute data of the user u, attribute data of the item i, and scoring data of the item i by the user u, the user u is over-scoring the item i Any one of the plurality of users, the item i being any one of the plurality of items;

The generating module 620 is configured to:

Processing a plurality of scoring records to obtain a training data set. Each training data in the training data set includes an identifier of the user u and a corresponding first interactive node list, an identifier of the item i, and a corresponding second interactive node list, and a user u pair. The score of item i.

The training module 650 is configured to:

Entering the identifier of the user u and the identifier of the item i into the feature learning model, and obtaining a feature vector corresponding to the user u and a feature vector corresponding to the item i, and the first corresponding to the user u Entering the feedback learning model by the interaction node list and the second interaction node list corresponding to the item i, and obtaining implicit feedback corresponding to the user u and implicit feedback corresponding to the item i;

And inputting the feature vector corresponding to the user u and the feature vector corresponding to the item i, the implicit feedback corresponding to the user u, and the implicit feedback corresponding to the item i into the neural network model to obtain a predicted score;

And performing the scoring model after the training according to the predicted score and the scoring of the item i by the user u, the feature learning model, the feedback learning model, and the neural network model.

Optionally, the first interaction node list corresponding to the user u includes a multi-level first interaction node list, and the second interaction node list corresponding to the item i includes a multi-level second interaction node list, where the feedback learning model is The model parameters include: a multi-level user feedback matrix and a multi-order item feedback matrix, wherein the order of the first interactive node list corresponding to the user u is the same as the order of the user feedback matrix, and the item i corresponds to the first The order of the two interactive node lists is the same as the order of the item feedback matrix, and the odd-order first interactive node list in the multi-level first interactive node list is used to represent the interaction information between the user and the item, and the multi-level first interactive node list The even-order first interaction node list is used to represent the interaction information between the user and other users, and the odd-order second interaction node list in the multi-level second interaction node list is used to represent the interaction information between the item and the user, and the multi-level second interaction node The even-order second interactive node list in the list is used to represent the interaction information of the item with other items;

The training module 650 is configured to:

Inputting the multi-step first interactive node list corresponding to the user u and the multi-level second interactive node list corresponding to the item i into the feedback learning model, to obtain implicit feedback corresponding to the user u and the item i Corresponding implicit feedback.

The training module 650 is configured to:

Entering the identifier of the user u and the corresponding first interaction node list, the identifier of the item i, and the corresponding second interaction node list into the feedback learning model, to obtain implicit feedback corresponding to the user u and the Implicit feedback corresponding to item i.

It should be noted that the foregoing obtaining module 610, the generating module 620, the scoring module 630, the determining module 640, and the training module 650 may be implemented by a processor, or the processor may be implemented by using a memory, or the processor may execute a program instruction in the memory. Implementation, or the processor is implemented with a memory and a transmitter.

It should be noted that, when the device of the recommended item provided by the foregoing embodiment is recommended, only the division of each functional module is used as an example. In an actual application, the function distribution may be completed by different functional modules as needed. The internal structure of the server is divided into different functional modules to complete all or part of the functions described above. In addition, the device for recommending the article provided in the above embodiment is the same as the method embodiment of the recommended article, and the specific implementation process is described in detail in the method embodiment, and details are not described herein again.

Based on the same technical concept, the embodiment of the present invention further provides a training device for scoring a model. As shown in FIG. 8, the device includes:

The obtaining module 810 is configured to obtain the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data, and specifically may implement the obtaining function in the foregoing step 501, and other implicit steps.

a generating module 820, configured to process attribute data of the plurality of users, attribute data of the plurality of items, and the scoring data to obtain a training data set, where the training data set includes an identifier and a corresponding of each user a first interactive node list, an identifier of each item, and a corresponding second interactive node list, each user scoring one or more items of the plurality of items, the first interactive node list being used to represent the user The interaction information with other users or items, the second interaction node list is used to indicate the interaction information of the item with other items or users, and specifically, the generation function in the above step 502, and other implicit steps can be implemented.

The training module 830 is configured to train the scoring model according to the training data set, and specifically implement the training function in the foregoing step 503, and other implicit steps.

Optionally, the attribute data of each of the multiple users further includes one or more of the following: gender, height, weight, age, occupation, income, hobbies, education, and the plurality of The attribute data of each item in the item further includes one or more of the following data: brand, color, size, price, comment, taste, shelf life, icon; the scoring data also includes one or more of the following data: Kind: operating time, current equipment, discounts.

Optionally, the obtaining module 810 is configured to:

The generating module 820 is configured to:

The training module 830 is configured to:

It should be noted that the foregoing obtaining module 810, the generating module 820, and the training module 830 may be implemented by a processor, or the processor may be implemented by using a memory, or the processor may execute a program instruction in the memory, or the processor cooperates with the memory. The transmitter is implemented.

The training device of the scoring model provided by the above embodiment is only illustrated by the division of the above functional modules when training the scoring model. In actual applications, the function allocation may be completed by different functional modules as needed, that is, the server The internal structure is divided into different functional modules to perform all or part of the functions described above. In addition, the training device of the scoring model and the training method of the scoring model provided by the above embodiments are in the same concept, and the specific implementation process is described in detail in the method embodiment, and details are not described herein again.

A person skilled in the art may understand that all or part of the steps of implementing the above embodiments may be completed by hardware, or may be instructed by a program to execute related hardware, and the program may be stored in a computer readable storage medium. The storage medium mentioned may be a read only memory, a magnetic disk or an optical disk or the like.

The above is only one embodiment of the present invention, and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application are included in the scope of the present application. Inside.

Claims

A method of recommending an article, the method comprising:

Obtaining attribute data of the target user and attribute data of the plurality of candidate items, the attribute data of the target user includes an identifier of the target user, and the attribute data of each candidate item includes an identifier of the corresponding candidate item;

Processing the attribute data of the target user and the attribute data of the plurality of candidate items to generate a target data set, where the target data set includes an identifier of the target user and a corresponding target first interaction node list, the An identifier of each candidate item of the plurality of candidate items and a corresponding target second interaction node list, wherein the target first interaction node list is used to represent interaction information of the target user with other users or items, and the target second The interactive node list is used to indicate interaction information of the candidate item with other items or users;

Entering the target data set into the scoring model to obtain scoring of the plurality of candidate items by the target user, wherein the scoring model is trained according to attribute data of multiple users, attribute data of multiple items, and scoring data. The attribute data of each of the plurality of users includes an identifier of the corresponding user, the attribute data of each of the plurality of items includes an identifier of the corresponding item, and the scoring data includes the plurality of users Each user scores one or more of the plurality of items;

Determining the target recommended item according to the target user's scoring of the plurality of candidate items.
The method according to claim 1, wherein the attribute data of the target user further comprises one or more of the following data: gender, height, weight, age, occupation, income, hobbies, education, each The attribute data of a candidate item also includes one or more of the following data: brand, color, size, price, comment, taste, shelf life, icon.
The method according to claim 1, wherein the processing the attribute data of the target user and the attribute data of the plurality of candidate items to generate a target data set comprises:

Determining, according to the identifier of the target user, a target first interaction node list corresponding to the target user in a target first interaction node list corresponding to the identifier of each user of the plurality of pre-recorded users, and according to each candidate Determining, in the target second interaction node list corresponding to the identifier of each candidate item among the plurality of candidate items recorded in advance, determining a target second interaction node list corresponding to each candidate item;

And generating a target data set according to the identifier of the target user, the target first interaction node list corresponding to the target user, the identifier of each candidate item, and the target second interaction node list corresponding to each candidate item.
The method according to claim 1, wherein the scoring model comprises a feature learning model, a feedback learning model, and a neural network model;

The step of inputting the target data set into the scoring model to obtain the scoring of the plurality of candidate items by the target user includes:

Inputting the identifier of the target user and the identifier of the candidate item j in the target data set into the feature learning model, obtaining a feature vector corresponding to the target user and a feature vector corresponding to the candidate item j, and using the target data Concentrating the target first interaction node list corresponding to the target user and the target second interaction node list corresponding to the candidate item j, inputting the feedback learning model, obtaining implicit feedback corresponding to the target user, and the candidate An implicit feedback corresponding to the item j, wherein the item j is any one of the plurality of candidate items;

And inputting a feature vector corresponding to the target user, a feature vector corresponding to the candidate item j, an implicit feedback corresponding to the target user, and an implicit feedback corresponding to the candidate item j into a neural network model to obtain the target The user scores the candidate item j.
The method according to claim 4, wherein the target first interaction node list comprises a multi-level target first interaction node list, and the target second interaction node list corresponding to each candidate item comprises a multi-level target second interaction Node list, multi-level target, first-order interaction node list, odd-order target, first interaction node list, used to represent interaction information of the target user and the item, and the multi-level target first interaction node list, the even-order target, the first interaction node list, is used for Indicates interaction information between the target user and other users. The odd-order target second interactive node list in the multi-level target second interaction node list is used to represent the interaction information between the candidate item and the user, and the even-order target in the multi-level target second interaction node list. The second interactive node list is used to indicate interaction information of the candidate item with other items;

And inputting the target first interaction node list corresponding to the target user in the target data set and the target second interaction node list corresponding to the candidate item j into the feedback learning model to obtain an implicit type corresponding to the target user Feedback and implicit feedback corresponding to candidate j, including:

And inputting the feedback learning model to the multi-level target first interaction node list corresponding to the target user and the multi-level target second interaction node list corresponding to the candidate item j in the target data set, to obtain the corresponding target user Implicit feedback and implicit feedback corresponding to candidate j.
The method according to claim 4, wherein the model parameters of the feedback learning model comprise: a weight of a feedback vector of each of the plurality of users, and a feedback vector of each of the plurality of items the weight of;

And inputting the target first interaction node list corresponding to the target user in the target data set and the target second interaction node list corresponding to the candidate item j into the feedback learning model to obtain an implicit type corresponding to the target user Feedback and implicit feedback corresponding to candidate j, including:

And inputting the identifier of the target user in the target data set and the corresponding target first interaction node list, the identifier of the candidate item j, and the corresponding target second interaction node list into the feedback learning model to obtain the target user. The corresponding implicit feedback and the implicit feedback corresponding to the candidate item j.
The method according to claim 1, wherein the determining the target recommended item according to the scoring of the plurality of candidate items by the target user comprises:

And determining, according to the scoring of the plurality of candidate items by the target user, the corresponding recommended item that meets the preset recommendation condition.
The method according to claim 7, wherein the determining, according to the scoring of the plurality of candidate items by the target user, determining that the corresponding score meets the target recommended item that meets the preset recommendation condition comprises:

Determining, according to the scoring of the plurality of candidate items by the target user, a preset number of target recommended items with a maximum score; or

And determining, according to the scoring of the plurality of candidate items by the target user, a target recommended item whose corresponding score is greater than a preset score threshold.
The method according to any one of claims 1-8, wherein the scoring model is trained by:

Obtaining attribute data of the plurality of users, attribute data of the plurality of items, and the scoring data;

Processing the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data to obtain a training data set, where the training data set includes an identifier of each user and a corresponding first interactive node list And an identifier of each item and a corresponding second interactive node list, each user scoring one or more items of the plurality of items, the first interactive node list being used to represent the user and other users or items Interactive information, the second interactive node list is used to indicate interaction information of the item with other items or users;

The scoring model is trained according to the training data set.
The method according to claim 9, wherein the attribute data of each of the plurality of users further comprises one or more of the following: sex, height, weight, age, occupation, income, The hobby, the educational situation, the attribute data of each of the plurality of items further includes one or more of the following data: brand, color, size, price, comment, taste, shelf life, icon; the scoring data is further Includes one or more of the following data: operating time, current equipment usage, discounts.
The method according to claim 9, wherein the obtaining the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data comprises:

Acquiring a plurality of scoring records, each of the plurality of scoring records including attribute data of the user u, attribute data of the item i, and scoring data of the item i by the user u, the user u is over-scoring the item i Any one of the plurality of users, the item i being any one of the plurality of items;

And processing the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data to obtain a training data set, including:

Processing a plurality of scoring records to obtain a training data set. Each training data in the training data set includes an identifier of the user u and a corresponding first interactive node list, an identifier of the item i, and a corresponding second interactive node list, and a user u pair. The score of item i.
The method according to claim 11, wherein the scoring model comprises a feature learning model, a feedback learning model, and a neural network model;

The training of the scoring model according to the training data set includes:

Entering the identifier of the user u and the identifier of the item i into the feature learning model, and obtaining a feature vector corresponding to the user u and a feature vector corresponding to the item i, and the first corresponding to the user u Entering the feedback learning model by the interaction node list and the second interaction node list corresponding to the item i, and obtaining implicit feedback corresponding to the user u and implicit feedback corresponding to the item i;

And inputting the feature vector corresponding to the user u and the feature vector corresponding to the item i, the implicit feedback corresponding to the user u, and the implicit feedback corresponding to the item i into the neural network model to obtain a predicted score;

And performing the scoring model after the training according to the predicted score and the scoring of the item i by the user u, the feature learning model, the feedback learning model, and the neural network model.
The method according to claim 12, wherein the first interactive node list corresponding to the user u comprises a multi-level first interactive node list, and the second interactive node list corresponding to the item i comprises a multi-level second interaction a node list, the model parameters of the feedback learning model include: a multi-level user feedback matrix and a multi-order item feedback matrix, wherein an order of the first interactive node list corresponding to the user u and an order of the user feedback matrix Similarly, the order of the second interactive node list corresponding to the item i is the same as the order of the item feedback matrix, and the odd-order first interactive node list in the multi-level first interactive node list is used to represent the interaction between the user and the item. Information, the even-order first interaction node list in the multi-level first interaction node list is used to represent the interaction information of the user with other users, and the odd-order second interaction node list in the multi-level second interaction node list is used to represent the item and the user. The interaction information, the even-order second interaction node list in the multi-level second interaction node list is used to represent the interaction information of the item with other items;

The first interactive node list corresponding to the user u and the second interactive node list corresponding to the item i are input into the feedback learning model, and the implicit feedback corresponding to the user u is obtained corresponding to the item i. Implicit feedback, including:

Inputting the multi-step first interactive node list corresponding to the user u and the multi-level second interactive node list corresponding to the item i into the feedback learning model, to obtain implicit feedback corresponding to the user u and the item i Corresponding implicit feedback.
The method according to claim 12, wherein the model parameters of the feedback learning model comprise: a weight of a feedback vector of each of the plurality of users, a feedback vector of each of the plurality of items the weight of;

The first interactive node list corresponding to the user u and the second interactive node list corresponding to the item i are input into the feedback learning model, and the implicit feedback corresponding to the user u is obtained corresponding to the item i. Implicit feedback, including:

Entering the identifier of the user u and the corresponding first interaction node list, the identifier of the item i, and the corresponding second interaction node list into the feedback learning model, to obtain implicit feedback corresponding to the user u and the Implicit feedback corresponding to item i.
A device for recommending articles, characterized in that the device comprises:

An obtaining module, configured to acquire attribute data of the target user and attribute data of the plurality of candidate items, where the attribute data of the target user includes an identifier of the target user, and the attribute data of each candidate item includes an identifier of the corresponding candidate item;

a generating module, configured to process attribute data of the target user and attribute data of the plurality of candidate items to generate a target data set, where the target data set includes an identifier of the target user and a corresponding target first interaction a node list, an identifier of each candidate item in the plurality of candidate items, and a corresponding target second interaction node list, where the target first interaction node list is used to represent interaction information between the target user and other users or items, The target second interaction node list is used to represent interaction information of the candidate item with other items or users;

a scoring module, configured to input the target data set into a scoring model, to obtain scoring of the plurality of candidate items by the target user, wherein the scoring model is based on attribute data of multiple users, attribute data of multiple items And the scoring data training, the attribute data of each of the plurality of users includes an identifier of the corresponding user, and the attribute data of each item of the plurality of items includes an identifier of the corresponding item, and the scoring data includes Each of the plurality of users scores one or more of the plurality of items;

a determining module, configured to determine a target recommended item according to the target user's scoring of the plurality of candidate items.
The device according to claim 15, wherein the attribute data of the target user further comprises one or more of the following data: gender, height, weight, age, occupation, income, hobbies, education, and each The attribute data of a candidate item also includes one or more of the following data: brand, color, size, price, comment, taste, shelf life, icon.
The device according to claim 15, wherein the generating module is configured to:

Determining, according to the identifier of the target user, a target first interaction node list corresponding to the target user in a target first interaction node list corresponding to the identifier of each user of the plurality of pre-recorded users, and according to each candidate Determining, in the target second interaction node list corresponding to the identifier of each candidate item among the plurality of candidate items recorded in advance, determining a target second interaction node list corresponding to each candidate item;

And generating a target data set according to the identifier of the target user, the target first interaction node list corresponding to the target user, the identifier of each candidate item, and the target second interaction node list corresponding to each candidate item.
The apparatus according to claim 15, wherein said scoring model comprises a feature learning model, a feedback learning model, and a neural network model;

Among them, the scoring module is used to:

Inputting the identifier of the target user and the identifier of the candidate item j in the target data set into the feature learning model, obtaining a feature vector corresponding to the target user and a feature vector corresponding to the candidate item j, and using the target data Concentrating the target first interaction node list corresponding to the target user and the target second interaction node list corresponding to the candidate item j, inputting the feedback learning model, obtaining implicit feedback corresponding to the target user, and the candidate An implicit feedback corresponding to the item j, wherein the item j is any one of the plurality of candidate items;

And inputting a feature vector corresponding to the target user, a feature vector corresponding to the candidate item j, an implicit feedback corresponding to the target user, and an implicit feedback corresponding to the candidate item j into a neural network model to obtain the target The user scores the candidate item j.
The apparatus according to claim 18, wherein the target first interaction node list comprises a multi-level target first interaction node list, and the target second interaction node list corresponding to each candidate item comprises a multi-level target second interaction Node list, multi-level target, first-order interaction node list, odd-order target, first interaction node list, used to represent interaction information of the target user and the item, and the multi-level target first interaction node list, the even-order target, the first interaction node list, is used for Indicates interaction information between the target user and other users. The odd-order target second interactive node list in the multi-level target second interaction node list is used to represent the interaction information between the candidate item and the user, and the even-order target in the multi-level target second interaction node list. The second interactive node list is used to indicate interaction information of the candidate item with other items;

The scoring module is configured to:

And inputting the feedback learning model to the multi-level target first interaction node list corresponding to the target user and the multi-level target second interaction node list corresponding to the candidate item j in the target data set, to obtain the corresponding target user Implicit feedback and implicit feedback corresponding to candidate j.
The apparatus according to claim 18, wherein the model parameter of the feedback learning model comprises: a weight of a feedback vector of each of the plurality of users, and a feedback vector of each of the plurality of items the weight of;

The scoring module is configured to:

And inputting the identifier of the target user in the target data set and the corresponding target first interaction node list, the identifier of the candidate item j, and the corresponding target second interaction node list into the feedback learning model to obtain the target user. The corresponding implicit feedback and the implicit feedback corresponding to the candidate item j.
The device according to claim 15, wherein the determining module is configured to:

And determining, according to the scoring of the plurality of candidate items by the target user, the corresponding recommended item that meets the preset recommendation condition.
The device according to claim 21, wherein the determining module is configured to:

Determining, according to the scoring of the plurality of candidate items by the target user, a preset number of target recommended items with a maximum score; or

And determining, according to the scoring of the plurality of candidate items by the target user, a target recommended item whose corresponding score is greater than a preset score threshold.
The device according to any one of claims 15 to 22, wherein the obtaining module is further configured to:

Obtaining attribute data of the plurality of users, attribute data of the plurality of items, and the scoring data;

The generating module is further configured to:

Processing the attribute data of the plurality of users, the attribute data of the plurality of items, and the scoring data to obtain a training data set, where the training data set includes an identifier of each user and a corresponding first interactive node list And an identifier of each item and a corresponding second interactive node list, each user scoring one or more items of the plurality of items, the first interactive node list being used to represent the user and other users or items Interactive information, the second interactive node list is used to indicate interaction information of the item with other items or users;

The device also includes:

A training module is configured to train the scoring model according to the training data set.
The apparatus according to claim 23, wherein the attribute data of each of the plurality of users further comprises one or more of the following: sex, height, weight, age, occupation, income, The hobby, the educational situation, the attribute data of each of the plurality of items further includes one or more of the following data: brand, color, size, price, comment, taste, shelf life, icon; the scoring data is further Includes one or more of the following data: operating time, current equipment usage, discounts.
The device according to claim 23, wherein the obtaining module is configured to:

Acquiring a plurality of scoring records, each of the plurality of scoring records including attribute data of the user u, attribute data of the item i, and scoring data of the item i by the user u, the user u is over-scoring the item i Any one of the plurality of users, the item i being any one of the plurality of items;

The generating module is configured to:

Processing a plurality of scoring records to obtain a training data set. Each training data in the training data set includes an identifier of the user u and a corresponding first interactive node list, an identifier of the item i, and a corresponding second interactive node list, and a user u pair. The score of item i.
The apparatus according to claim 25, wherein said scoring model comprises a feature learning model, a feedback learning model, and a neural network model;

The training module is configured to:

Entering the identifier of the user u and the identifier of the item i into the feature learning model, and obtaining a feature vector corresponding to the user u and a feature vector corresponding to the item i, and the first corresponding to the user u Entering the feedback learning model by the interaction node list and the second interaction node list corresponding to the item i, and obtaining implicit feedback corresponding to the user u and implicit feedback corresponding to the item i;

And inputting the feature vector corresponding to the user u and the feature vector corresponding to the item i, the implicit feedback corresponding to the user u, and the implicit feedback corresponding to the item i into the neural network model to obtain a predicted score;

And performing the scoring model after the training according to the predicted score and the scoring of the item i by the user u, the feature learning model, the feedback learning model, and the neural network model.
The device according to claim 26, wherein the first interactive node list corresponding to the user u comprises a multi-level first interactive node list, and the second interactive node list corresponding to the item i comprises a multi-level second interaction a node list, the model parameters of the feedback learning model include: a multi-level user feedback matrix and a multi-order item feedback matrix, wherein an order of the first interactive node list corresponding to the user u and an order of the user feedback matrix Similarly, the order of the second interactive node list corresponding to the item i is the same as the order of the item feedback matrix, and the odd-order first interactive node list in the multi-level first interactive node list is used to represent the interaction between the user and the item. Information, the even-order first interaction node list in the multi-level first interaction node list is used to represent the interaction information of the user with other users, and the odd-order second interaction node list in the multi-level second interaction node list is used to represent the item and the user. The interaction information, the even-order second interaction node list in the multi-level second interaction node list is used to represent the interaction information of the item with other items;

The training module is configured to:

Inputting the multi-step first interactive node list corresponding to the user u and the multi-level second interactive node list corresponding to the item i into the feedback learning model, to obtain implicit feedback corresponding to the user u and the item i Corresponding implicit feedback.
The apparatus according to claim 26, wherein said model parameters of said feedback learning model comprise: a weight of a feedback vector of each of said plurality of users, a feedback vector of each of said plurality of items the weight of;

The training module is configured to:

Entering the identifier of the user u and the corresponding first interaction node list, the identifier of the item i, and the corresponding second interaction node list into the feedback learning model, to obtain implicit feedback corresponding to the user u and the Implicit feedback corresponding to item i.
An apparatus, comprising: a processor and a memory, the processor being configured to execute instructions stored in the memory; the processor executing the instructions to cause the apparatus to implement the claim of any of claims 1-14 Methods.
A computer readable storage medium, comprising instructions for causing a computer to perform the method of any of claims 1-14 when the computer readable storage medium is run on a computer .