CN115935067A - Article recommendation method integrating semantics and structural view for socialized recommendation - Google Patents
Article recommendation method integrating semantics and structural view for socialized recommendation Download PDFInfo
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Abstract
The invention discloses a socialized recommendation-oriented article recommendation method integrating semantics and structural views, which comprises the following steps of: 1, proposing definition of implicit relationship in social recommendation; 2, constructing a heterogeneous information network and defining a meta path; 3 extracting local score prediction; 4, extracting global scoring prediction; 5, model fusion; 6 put forward constraints on user rating behavior; and 7, training the model and obtaining a trained depth map model and a trained width linear attention model. The method can still ensure the accuracy and stability of recommendation under the conditions of different social relationship distribution unbalancedness and sparsity.
Description
Technical Field
The invention belongs to the field of social recommendation systems, and particularly relates to an article recommendation method of a semantic and structural view fusion model for social recommendation.
Background
Nowadays, social platforms are developed vigorously, the relation between people becomes compact, and how to perform modeling analysis on the relation between platform users and commodities to further realize more accurate social recommendation becomes increasingly important. Different from a traditional collaborative recommendation algorithm, the social recommendation system is based on a series of social influence theories, the main ideas are that users with explicit social relations often have similar preferences, and the choices of the users are also possibly influenced by relatives and friends of the users, and the main method is to take the relations in the social network as auxiliary information to improve the recommendation accuracy. Socialization recommendations have long penetrated various aspects of life, such as: the system comprises a commodity recommendation function in an e-commerce platform, a friend recommendation function in a friend making platform and the like. At present, the improvement of the social recommendation system performance is mainly limited by two factors of social relationship distribution imbalance, sparsity and rating behavior difference, so that the relative position relationship between a user and an article is kept while a large amount of latent data is further mined, the explicit-implicit relationship between the user and a commodity is more efficiently and reasonably utilized, and the method is the key for improving the robustness of the social recommendation system. The current recommendation methods associated with this model can be divided into three categories: 1. the classical social recommendation method mainly comprises a co-factor decomposition method, an integration method and a regularization method, wherein the methods only use a small amount of implicit data, and a plurality of high-quality implicit relations are still to be discovered; 2. the social recommendation system based on the graph model is characterized in that a high-order relation model is built based on the graph neural network, various data are better utilized, further mining of implicit relations is still lacked, and meanwhile, the influence of user scoring behaviors on recommendation results is ignored 3. The social recommendation method based on the multi-view aims at mining data information from multiple angles, and different interaction structures have different effects on scoring prediction.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the article recommendation method integrating the semantics and the structural view for social recommendation, so that the accuracy and the stability of article recommendation can be ensured under the conditions of different social relationship distribution unbalancedness and sparsity.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a socialized recommendation-oriented article recommendation method integrating semantics and structural views, which is characterized by comprising the following steps of:
step 1, defining implicit relation in social recommendation:
step 1.1, let U = { U = { (U) } 1 ，…，u i ，…，u M Denotes a set of users, u i Represents any ith user, i is more than or equal to 1 and less than or equal to M, and V = { V = 1 ，…，v a ，…，v N Denotes a collection of items, v a Representing any a-th article, wherein a is more than or equal to 1 and less than or equal to N;
let the user score matrix be R = { R = ia } M×N Represents the scoring of all items in item set V by all users in user set U, where r ia Representing an arbitrary ith user u i For any a item v a Scoring of (4);
let the user social matrix be denoted as S = { S = } ik } M×M Represents whether each user in the user set U focuses on other users, wherein s ik Represents an arbitrary i-th user u i Whether to pay attention to any k-th user u k If the ith user u i Focus on the kth user u k Then let s ik =1, otherwise, let s ik ＝0；
Let i user u i Is marked as F U (i)＝{u k |s ik =1}; k is more than or equal to 1 and less than or equal to M; and i is not equal to k;
step 1.2, order the ith user u i Is marked as HU (i) = { u) = ij ||||s ik ＝1∩s jk 1| ≧ τ }, where u | | is greater than or equal to τ }, where u ij Represents the ith user u i Implicit social friends of s ik ＝1∩s jk =1 denotes the i-th user u i K user u of interest k Pay attention to the jth user u at the same time j τ represents a cutoff threshold, 1 ≦ τ; j is more than or equal to 1 and less than or equal to M; and j ≠ k;
obtaining the a-th item v a And the b-th item v b User set U all giving scores ab ，U ab E is U; obtaining a user set U by using the formula (1) ab The ith user u i For the a-th item v a With the b-th item v b Score similarity between them
In the formula (1), r ib Represents the ith user u i For any b-th item v b Scoring; b is more than or equal to 1 and less than or equal to N; and a is not equal to b;
obtaining a user set U by using the formula (2) ab All users in the system to the a-th item v a With the b-th item v b Score similarity cumulative value sim between ab Thereby obtaining the a-th article v a A scoring similarity cumulative value set with other articles;
sorting related articles in descending order according to the score similarity accumulated value set, thereby obtaining the a-th article v a Hidden item relation set H V (a)；
Step 2, constructing a heterogeneous information network and defining a meta path:
respectively taking each user and each article as nodes, taking the social friend set and the recessive social friend set of each user as the explicit-implicit relationship among the nodes of each user, and taking the recessive article relationship set of each article as the implicit relationship among the nodes of each article, thereby constructing edges among the nodes and forming a heterogeneous information network HIN;
defining five user element paths including three user single-hop neighbor element paths and two user double-hop neighbor element paths; the three user single-hop neighbor paths formed by two nodes and one edge connected with the two nodes comprise: user-item, user-user, user-implicit user; wherein, the user-article represents the user node to the article node and one edge connected with the article node; user-user represents one user node to another user node and an edge connected with the user node; the user-hidden user represents one user node to another hidden user node and one edge connected with the user node;
two kinds of user double-hop neighbor paths consisting of three nodes and two edges connected with the three nodes comprise: user-item, user-implicit user-item; the user-object represents a user node to another user node and one edge connected with the user node, the second user node to an object node and one edge connected with the object node, the user-hidden user-object represents a user node to a hidden user node and one edge connected with the hidden user node, and the hidden user node to an object node and one edge connected with the hidden user node;
three item meta-paths are defined, including: item-user, item-cryptic-user; wherein article-user represents an article node to a user node and an edge connecting the article-hidden article represents an article node to a hidden article node and an edge connecting the article node to the hidden article node, article-hidden article-user represents an article node to a hidden article node and an edge connecting the article node to the hidden article node, and a hidden article node to a user node and an edge connecting the hidden article node to the user node;
step 3, extracting local score prediction;
step 3.1, randomly removing partial nodes and associated edges in the heterogeneous information network HIN to obtain a preprocessed heterogeneous information network HIN';
step 3.2, obtaining ith user u from HIN' by using an embedded layer i Characteristic vector p of i ∈R d And the a-th item v a Characteristic vector q of a ∈R d′ D' represents a feature vector dimension;
3.3, building a depth map model consisting of a graph convolution network based on an attention mechanism, a user local feature extraction module, an article local feature extraction module and a local prediction module;
step 3.3.1, p i ，q a Inputting into a graph convolution network based on an attention mechanism for processing to obtain output vectors of five user element paths, wherein the output vectors comprise: user-item meta-path based embedded vector
Embedding vector based on user-user meta path +>
Embedded vector based on user-item meta-path>
Embedded vector based on user-implicit user meta-path ≥>
Embedded vector based on user-implicit user-item meta path>
Step 3.3.2, the user local feature extraction module processes the output vectors of the five user element paths by using the formula (2) to obtain the ith user u i Local feature embedding vector of
In the formula (2), the reaction mixture is,
indicating a collocated operation, MLP user Representing a multi-layer feedforward neural network in a user local feature extraction module;
step 3.3.3, the ith user u i Characteristic vector p of i And the a-th item v a Characteristic vector q of a Inputting into a graph convolution network based on an attention mechanism for processing to obtain output vectors of three article element paths, wherein the output vectors comprise: embedded vector based on item-user meta-path
Embedded vector based on item-implicit item meta-path>
Embedded vector based on item-implicit item-user meta path>
Step 3.3.4, the article local feature extraction module processes the output vectors of the three article element paths by using the formula (3) and outputs the a-th article v a Local feature embedding vector of
In formula (3), MLP item Representing an articleA multilayer feedforward neural network in the local feature extraction module;
step 3.3.5, mixing
Inputting into a local prediction module, and obtaining the ith user u by using an equation (4) i For the a-th item v a Partial score prediction in->
In formula (4), MLP deep Representing a multi-layer feedforward neural network in a local prediction module;
step 4, extracting global scoring prediction;
step 4.1, according to the ith user u i Set of implicit social friends of H U (i) Obtaining the ith user u from HIN by using embedded layer i Potential feature vector p' i ∈R d′ And potential influence vector x i ∈R d′ And according to the implicit item relation set H of the a-th item V (a) Obtaining the a-th item v from the HIN by using the embedding layer a Potential feature vector q' a ∈R d And potential influence vector y a ∈R d′ ；
4.2, building a wide linear attention model consisting of a user global feature extraction module, an article global feature extraction module and a global prediction module;
step 4.2.1, the user global feature extraction module obtains the ith user u by using the formulas (5) to (8) based on three user single-hop neighbor paths i Global feature embedded vector of
In the formula (8), α ik E alpha represents the ith user u i For the k user u k Attention weight of (1), beta ij E beta represents the ith user u i For the jth user u in the recessive social friends ij Attention weight of (1), γ ia E gamma represents the ith user u i For the a-th item v a Attention weight of (1), R V (i) E R represents the ith user u i A set of items for which a score has been given; w 1 ，W 2 ，W 3 ，W 4 ，W 5 ，W 6 For six trainable parameter matrices, b 1 ，b 2 ，b 3 Three offset vectors; sigma is an activation function, and softmax represents a normalization function; t represents transposition;
step 4.2.2, the item global feature extraction module obtains the a-th item v by using the formula (9) a Global feature embedding vector of
In the formula (9), eta ab E eta represents the a-th item v a For the b recessive similar item v b Attention weight of (1);
step 4.2.3, will
Inputting into a global prediction module, and obtaining the ith user u by using an equation (10) i For the a-th article v a Global of (2) score prediction>
In formula (10), b i For the ith user u i User deviation of (a), b a For the a-th item v a Mu is the average value of all users after adding the scores of all the articles;
step 5, obtaining the ith user u by using the formula (11) i For the a-th article v a Scoring the predicted outcome
In formula (11), λ represents a scoring weight coefficient;
step 6, constructing a constraint aiming at the scoring behavior of the user;
step 6.1, defining a rating triple (U, R, V) of a user to an item, wherein U belongs to U and represents a user entity, V belongs to V and represents an item entity, R belongs to R and represents a rating relation, and calculating a feature vector through a TransH algorithm, wherein the method comprises the following steps: user entity feature vector e u Article entity feature vector e v Ranking relation featuresEigenvector e r ；
Obtaining e according to formula (12) and formula (13) u ，e v Projection vector of user entity characteristic of hyperplane in relation r
And an item physical feature projection vector>
In formulae (12) and (13), w r Is a normal vector corresponding to the hyperplane;
step 6.2, constructing a scoring function f (u, r, v) of the rating triple (u, r, v) by using the formula (14):
step 6.3, constructing a marginal loss function L by using the formula (15) KG ：
In the formula (16), [ f (-)] + Represents max (0, f (·)), V 'represents another item entity, (u, r, V') represents a dummy triple generated by V 'in place of V, V' is e.v; f (u, r, v ') represents the scoring function of the false triplet (u, r, v');
step 7, constructing a loss function L of the depth map model by using the formula (14) D ：
In formula (14), λ 1 ，λ 2 For regularization parameters, P represents a matrix formed by all user characteristic vectors, and Q represents a matrix formed by all article characteristic vectors;
construction of the loss function L of the Width Linear attention model Using equation (15) W ：
In formula (15), λ 3 For regularization parameters, P 'represents a matrix formed by potential feature vectors of all users, Q' represents a matrix formed by potential feature vectors of all articles, X represents a matrix formed by potential influence vectors of all users, and Y represents a matrix formed by potential influences of all articles;
step 7, training the depth map model and the width linear attention model by using a gradient descent method respectively, and correspondingly calculating a loss function L D And a loss function L W Updating the model parameters until the loss function is converged, thereby obtaining a trained depth map model and a trained width linear attention model;
and 8, respectively inputting the item set, the user scoring matrix, the target user social friend set and the recessive social friend set of a certain target user into the trained depth map model and the trained width linear attention model, and correspondingly obtaining the local scoring prediction and the global scoring prediction of the target user, so that the scoring of the target user on the items is calculated by using the formula (11), and K items with the top scoring rank are selected and recommended to the target user, so that the item recommendation of the target user is completed.
The electronic device comprises a memory and a processor, and is characterized in that the memory is used for storing programs for supporting the processor to execute the item recommendation method, and the processor is configured to execute the programs stored in the memory.
The present invention relates to a computer-readable storage medium, having a computer program stored thereon, wherein the computer program is adapted to be executed by a processor to perform the steps of the item recommendation method.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the problem of unbalanced distribution and sparsity of social relations is solved to a certain extent by mining implicit relations between the users and the objects, the influence of deviation and overfitting caused by noise nodes on score prediction results is reduced by predicting and weighting global scores and local scores, meanwhile, the relative position relation between the users and the objects is guaranteed by constraining the user scores, the preference of the users is better reflected, the performance and the overall generalization capability of the model are improved, the more comprehensive understanding of the relation between the users and the objects by the model is guaranteed, and the accuracy and the stability of the recommendation results are further improved.
2. The invention provides a semantic and structural view fusion model for predicting user scoring behavior, which is characterized in that a heterogeneous information network is constructed by introducing the definition of implicit relationship, the extraction of the explicit and implicit relationship characteristics of a user and an article is realized based on two models with different views, the two models are fused with the information of global and local characteristics acquired, the user scoring constraint is integrated, the understanding of the preference of the user is deepened, and the improvement of the performance of the whole recommendation system is facilitated.
Drawings
FIG. 1 is a diagram of a heterogeneous information network for the method of the present invention;
FIG. 2 is a depth map model of the method of the present invention;
FIG. 3 is a broad linear attention model of the method of the present invention;
FIG. 4 is a flowchart of the method of the present invention.
Detailed Description
In this embodiment, a social recommendation oriented article recommendation method with fusion of semantics and structural views is performed according to the following steps as shown in fig. 4:
step 1, defining implicit relations in social recommendation:
step 1.1, let U = { U = { (U) } 1 ，…，u i ，…，u M Denotes a set of users, u i Represents an arbitrary ith user, 1 ≦ i ≦ M, V = { V ≦ M 1 ，…，v a ，…，v N Denotes a collection of items, v a Representing any a-th article, wherein a is more than or equal to 1 and less than or equal to N;
let the user score matrix be R = { R = ia } M×N Represents the scoring of all items in item set V by all users in user set U, where r ia Representing an arbitrary ith user u i For any a item v a Scoring of (4);
let the user social matrix be denoted as S = { S = } ik } M×M Represents whether each user in the user set U focuses on other users, wherein s ik Represents an arbitrary i-th user u i Whether to pay attention to any k-th user u k If the ith user u i Focus on the kth user u k Then let s ik =1, otherwise, let s ik ＝0；
Let i user u i Is marked as F U (i)＝{u k |s ik =1}; k is more than or equal to 1 and less than or equal to M; and i is not equal to k;
step 1.2, order the ith user u i The set of implicit social friends is marked as H U (i)＝{u ij |||s ik ＝1∩s jk 1| ≧ τ }, where u | | is greater than or equal to τ }, where u ij Represents the ith user u i Implicit social friends of s ik ＝1∩s jk =1 denotes the i-th user u i Kth user u of interest k Pay attention to the jth user u at the same time j τ represents a cutoff threshold, 1 ≦ τ; j is more than or equal to 1 and less than or equal to M; and j ≠ k; the set of implicit social friends may interpret that there may be similar preferences between users who possess a common follower, with a larger τ indicating more common followers needed to establish implicit user relationships.
Obtaining the a-th item v a And the b-th item v b User set U all giving scores ab ，U ab E is U; obtaining a user set U by using the formula (1) ab To middlei users u i For the a-th item v a With the b-th item v b Score similarity between them
In the formula (1), r ib Represents the ith user u i For any b-th item v b Scoring of (4); b is more than or equal to 1 and less than or equal to N; and a is not equal to b;
obtaining a user set U by using the formula (2) ab All users in the system to the a-th item v a With the b-th item v b Score similarity cumulative value sim between ab Thereby obtaining the a-th article v a A scoring similarity cumulative value set with other articles;
sorting related articles in descending order according to the score similarity accumulated value set, thereby obtaining the a-th article v a Hidden item relation set H V (a) (ii) a The set of implicit item relationships may be interpreted as the more similar the common user scores of the two items are, the higher the similarity of the two items is.
Step 2, constructing a heterogeneous information network and defining a meta path:
as shown in fig. 1, each user and each article are respectively used as nodes, the social friend set and the recessive social friend set of each user are used as explicit and implicit relations between nodes of each user, and the recessive article relation set of each article is used as the implicit relation between nodes of each article, so that edges between the nodes are constructed, and a heterogeneous information network HIN is formed;
defining five user element paths including three user single-hop neighbor element paths and two user double-hop neighbor element paths; the three user single-hop neighbor paths formed by two nodes and one edge connected with the two nodes comprise: user-item, user-user, user-implicit user; wherein, the user-article represents the user node to the article node and one edge connected with the article node; user-user means from one user node to another user node and an edge connected thereto; the user-hidden user represents one user node to another hidden user node and one edge connected with the user node;
two kinds of user double-hop neighbor paths consisting of three nodes and two edges connected with the three nodes comprise: user-item, user-implicit user-item; the user-object represents a user node to another user node and one edge connected with the user node, the second user node to an object node and one edge connected with the object node, the user-hidden user-object represents a user node to a hidden user node and one edge connected with the hidden user node, and the hidden user node to an object node and one edge connected with the hidden user node;
defining three item meta-paths including: item-user, item-covert item-user; the system comprises an article node, a user node, an article-implicit article node, a user node and a connecting edge of the user node, the article-implicit article node, the connecting edge of the implicit article node and the user node, the article-implicit article-user node, the user node and the connecting edge of the user node, wherein the article-user represents an article node to a user node and a connecting edge of the user node;
step 3, extracting local score prediction;
step 3.1, randomly removing partial nodes and associated edges in the heterogeneous information network HIN to obtain a preprocessed heterogeneous information network HIN'; in specific implementation, after nodes and associated edges are randomly removed by using a DropNode, the capacity of a user-item double-edge graph is 30, the capacity of a user social friend is 20, and the top 20 users and items are selected as implicit user and item relationships.
Step 3.2, obtaining ith user u from HIN' by using an embedded layer i Characteristic vector p of i ∈R d And the a-th articlev a Characteristic vector q of a ∈R d′ D' represents a feature vector dimension;
step 3.3, as shown in FIG. 2, building a depth map model composed of a graph convolution network based on an attention mechanism, a user local feature extraction module, an article local feature extraction module and a local prediction module; embedding sizes =80, dropout ratio =0.5, and slope of the activation function leak Relu is 0.2 for the convolutional neural network.
Step 3.3.1, p i ，q a Inputting into a graph convolution network based on an attention mechanism for processing to obtain output vectors of five user element paths, wherein the output vectors comprise: user-item meta-path based embedded vector
Embedding vector based on user-user meta path +>
Embedded vector based on user-item meta path>
Embedded vector based on user-implicit user meta-path ≥>
Embedded vector based on user-implicit user-item meta path>
Step 3.3.2, the user local feature extraction module processes the output vectors of the five user element paths by using the formula (2) to obtain the ith user u i Local feature embedding vector of
In the formula (2), the reaction mixture is,
indicating a collocated operation, MLP user Representing a multi-layer feedforward neural network in a user local feature extraction module; />
Step 3.3.3, the ith user u i Characteristic vector p of i And the a-th item v a Characteristic vector q of a Inputting into a graph convolution network based on an attention mechanism for processing to obtain output vectors of three article meta-paths, wherein the output vectors comprise: embedded vector based on item-user meta-path
Embedded vector based on item-implicit item meta-path>
Embedded vector based on item-implicit item-user meta path>
Step 3.3.4, the article local feature extraction module processes the output vectors of the three article element paths by using the formula (3) and outputs the a-th article v a Local feature embedding vector of
In formula (3), MLP item Representing a multi-layer feedforward neural network in an article local feature extraction module;
step 3.3.5, mixing
Inputting into a local prediction module, and obtaining the ith user u by using an equation (4) i For the a-th item v a Is predicted to be->
In formula (4), MLP deep Representing a multi-layer feedforward neural network in a local prediction module;
step 4, extracting global scoring prediction;
step 4.1, according to the ith user u i Set of implicit social friends of H U (i) Obtaining the ith user u from HIN by using embedded layer i Potential feature vector p' i ∈R d′ And potential influence vector x i ∈R d′ And according to the recessive article relation set H of the a-th article V (a) Obtaining the a-th item v from the HIN by using the embedding layer a Potential feature vector q 'of' a ∈R d And potential influence vector y a ∈R d′ ；
Step 4.2, as shown in fig. 3, building a wide linear attention model composed of a user global feature extraction module, an article global feature extraction module and a global prediction module;
step 4.2.1, the user global feature extraction module obtains the ith user u by using the formulas (5) to (8) based on three user single-hop neighbor paths i Global feature embedding vector of
In the formula (8), α ik E α represents the ith user u i For the k user u k Attention weight of (1), beta ij E beta represents the ith user u i For the jth user u in the recessive social friends ij Attention weight of (1), gamma ia E x represents the ith user u i For the a-th item v a Attention weight of (1), R V (i) E R represents the ith user u i A set of items for which a score has been given; w is a group of 1 ，W 2 ，W 3 ，W 4 ，W 5 ，W 6 Six trainable parameter matrices, b 1 ，b 2 ，b 3 Three offset vectors; sigma is an activation function, and softmax represents a normalization function; t represents transposition;
step 4.2.2, the global feature extraction module of the article obtains the a-th article v by using the formula (9) a Global feature embedded vector of
/>
In the formula (9), eta ab E eta represents the a-th item v a For the b-th recessive similar item v b Attention weight of (1);
step 4.2.3, will
Inputting the data into a global prediction module, and obtaining the ith user u by using an equation (10) i For the a-th item v a Is predicted based on the global score of->
In the formula (10), b i For the ith user u i User deviation of (a), b a Is the a-th item v a Mu is the average value of all users after adding the scores of all the articles; in specific implementation, trustSVD algorithm can be directly used, and
linear combination is performed.
Step 5, obtaining the ith user u by using the formula (11) i For the a-th item v a Scoring the predicted outcome
In the formula (11), λ represents a scoring weight coefficient;
step 6, constructing a constraint aiming at the scoring behavior of the user;
step 6.1, defining a rating triple (U, R, V) of a user to an item, wherein U belongs to U and represents a user entity, V belongs to V and represents an item entity, R belongs to R and represents a rating relation, and calculating a feature vector through a TransH algorithm, wherein the method comprises the following steps: user entity feature directionQuantity e u Article entity feature vector e v Ranking the relational feature vector e r ；
Obtaining e according to formula (12) and formula (13) u ，e v Feature projection vector of hyperplane at relation r
In formulae (12) and (13), w r A normal vector corresponding to the hyperplane; t represents transposition;
step 6.2, constructing a scoring function f (u, r, v) of the rating triple (u, r, v) by using the formula (14):
step 6.3, constructing a marginal loss function L by using the formula (15) KG ：
In the formula (16), [ f (-)] + Represents max (0, f (·)), V 'represents another item entity, (u, r, V') represents a false triple generated by V 'in place of V, V' is epsilon V; f (u, r, v ') represents the scoring function of the false triplet (u, r, v'); in a specific implementation, a score-based cross-sampling substitution method is adopted to generate a false triple, and all false items/users in the false triple should be sampled from rated items/users with ratings smaller than the ratings in the real three groups.
Step 7, constructing a loss function L of the depth map model by using the formula (14) D ：
In the formula (14), λ 1 ，λ 2 For regularization parameters, P represents a matrix formed by all user characteristic vectors, and Q represents a matrix formed by all article characteristic vectors;
construction of the loss function L of the Wide Linear attention model Using equation (15) W ：
In formula (15), λ 3 For regularizing parameters, P 'represents a matrix formed by potential characteristic vectors of all users, Q' represents a matrix formed by potential characteristic vectors of all articles, X represents a matrix formed by potential influence vectors of all users, and Y represents a matrix formed by potential influences of all articles; in specific implementation, different embedding parameters are respectively given to the depth map model and the width linear attention model so as to enhance the flexibility of the fusion model. The learning rate =0.001 for the depth map model and the learning rate =0.05 for the width linear attention model regularization parameter λ 1 ＝2，λ 2 ＝0.0001，λ 3 ＝0.05。
Step 7, training the depth map model and the width linear attention model respectively by using a gradient descent method, and correspondingly calculating a loss function L D And a loss function L W Updating the model parameters until the loss function is converged, thereby obtaining a trained depth map model and a trained width linear attention model;
step 8, as shown in fig. 4, for a certain target user, respectively inputting an article set, a user scoring matrix, a target user social friend set and a recessive social friend set into the trained depth map model and the trained width linear attention model, and correspondingly obtaining a local scoring prediction and a global scoring prediction, so as to calculate the score of the target user for the article by using formula (11), and select a K article with the top scoring rank to recommend to the target user, thereby completing the article recommendation of the target user.
In this embodiment, an electronic device includes a memory for storing a program that supports a processor to execute the item recommendation method, and a processor configured to execute the program stored in the memory.
In this embodiment, a computer-readable storage medium stores a computer program, and the computer program is executed by a processor to execute the steps of the item recommendation method.
In conclusion, the method further enhances the robustness and generalization capability on the basis of the early socialized recommendation model by utilizing the semantic and structural view fusion-based model, simultaneously considers the characteristic learning, model fusion and user scoring relative position relation based on the explicit-implicit relation, and can still ensure the accuracy and stability of recommendation under the conditions of imbalance and sparsity of different social relation distributions.
Claims (3)
1. A socialized recommendation-oriented item recommendation method integrating semantics and structural views is characterized by comprising the following steps of:
step 1, defining implicit relations in social recommendation:
step 1.1, let U = { U = { (U) } 1 ,···,u i ,···,u M Denotes a set of users, u i Represents an arbitrary ith user, 1 ≦ i ≦ M, V = { V ≦ M 1 ,···,v a ,···,v N Denotes a collection of items, v a Representing any a-th article, wherein a is more than or equal to 1 and less than or equal to N;
let the user score matrix be R = { R = ia } M×N Represents the scoring of all items in item set V by all users in user set U, where r ia Representing an arbitrary ith user u i For any a item v a Scoring of (4);
let the user social matrix be denoted as S = { S = } ik } M×M Represents whether each user in the user set U is interested in other users, wherein s ik Indicates whether an arbitrary ith user ui is paying attention to an arbitrary kth user u k If the ith user u i Focus on the kth user u k Then let s ik =1, otherwise, let s ik ＝0；
Let i user u i Is marked as F U (i)＝{u k |s ik =1}; k is more than or equal to 1 and less than or equal to M; and i is not equal to k;
step 1.2, order the ith user u i The set of implicit social friends is marked as H U (i)＝{u ij |||s ik ＝1∩s jk =1| ≧ epsilon }, where u ij Represents the ith user u i Implicit social friends of s ik ＝1∩s jk =1 denotes the i-th user u i Kth user u of interest k Pay attention to the jth user u at the same time j τ represents a cutoff threshold, 1 ≦ τ; j is more than or equal to 1 and less than or equal to M; and j ≠ k;
obtaining the a-th item v a And the b-th item v b User set U all giving scores ab ，U ab Belongs to U; obtaining a user set U by using the formula (1) ab The ith user u i For the a-th item v a With the (b) th item v b Score similarity between them
In the formula (1), r ib Represents the ith user u i For any b-th item v b Scoring of (4); b is more than or equal to 1 and less than or equal to U; and a is not equal to b;
obtaining a user set U by using the formula (2) ab All users in the system to the a-th item v a With the b-th item v b Score similarity cumulative value sim between ab FromTo obtain the a-th article v a A set of score similarity cumulative values with other items;
sorting related articles in descending order according to the score similarity accumulated value set, thereby obtaining the a-th article v a Hidden item relation set H V (a)；
Step 2, constructing a heterogeneous information network and defining a meta path:
respectively taking each user and each article as nodes, taking the social friend set and the recessive social friend set of each user as the explicit-implicit relationship among the user nodes, and taking the recessive article relationship set of each article as the implicit relationship among the article nodes, thereby constructing edges among the nodes and forming a heterogeneous information network HIN;
defining five user element paths including three user single-hop neighbor element paths and two user double-hop neighbor element paths; the three user single-hop neighbor paths formed by two nodes and one edge connected with the two nodes comprise: user-item, user-user, user-implicit user; wherein, the user-item represents the user node to the item node and one edge connected with the item node; user-user represents one user node to another user node and an edge connected with the user node; the user-hidden user represents one user node to another hidden user node and one edge connected with the user node;
two kinds of user double-hop neighbor paths composed of three nodes and two edges connected with the three nodes comprise: user-item, user-implicit user-item; the user-object represents a user node to another user node and one edge connected with the user node, the second user node to an object node and one edge connected with the object node, the user-hidden user-object represents a user node to a hidden user node and one edge connected with the hidden user node, and the hidden user node to an object node and one edge connected with the hidden user node;
three item meta-paths are defined, including: item-user, item-cryptic-user; the system comprises an article node, a user node, an article-implicit article node, a user node and a connecting edge of the user node, the article-implicit article node, the connecting edge of the implicit article node and the user node, the article-implicit article-user node, the user node and the connecting edge of the user node, wherein the article-user represents an article node to a user node and a connecting edge of the user node;
step 3, extracting local score prediction;
step 3.1, randomly removing partial nodes and associated edges in the heterogeneous information network HIN to obtain a preprocessed heterogeneous information network HIN';
step 3.2, obtaining ith user u from HIN' by using an embedded layer i Characteristic vector p of i ∈R d' And the a-th item v a Characteristic vector q of a ∈R d' D' represents a feature vector dimension;
3.3, building a depth map model consisting of a graph convolution network based on an attention mechanism, a user local feature extraction module, an article local feature extraction module and a local prediction module;
step 3.3.1, p i ，q a Inputting into a graph convolution network based on an attention mechanism for processing to obtain output vectors of five user element paths, wherein the output vectors comprise: user-item meta-path based embedded vector
Embedded vector based on user-user meta path ≥>
Embedded vector based on user-item meta path>
User-implicit user meta-path based embedded vector
Embedded vector based on user-implicit user-item meta-path>
Step 3.3.2, the user local feature extraction module processes the output vectors of the five user element paths by using the formula (2) to obtain the ith user u i Local feature embedding vector of
In the formula (2), ≧ denotes a concatenation operation, MLP user Representing a multi-layer feedforward neural network in a user local feature extraction module;
step 3.3.3, the ith user u i Characteristic vector p of i And the a-th item v a Characteristic vector q of a Inputting into a graph convolution network based on an attention mechanism for processing to obtain output vectors of three article meta-paths, wherein the output vectors comprise: embedded vector based on item-user meta-path
Embedded vector based on item-implicit item meta-path>
Embedded vector based on item-implicit item-user meta path>
Step 3.3.4, the article local feature extraction module utilizes the formula (3) to carry out vector summation on output vectors of three article element pathsLine processing to output the a-th item v a Local feature embedding vector of
In formula (3), MLP item Representing a multi-layer feedforward neural network in an article local feature extraction module;
step 3.3.5, mixing
Inputting into a local prediction module, and obtaining the ith user u by using an equation (4) i For the a-th item v a Partial score prediction in->
In formula (4), MLP deep Representing a multi-layer feedforward neural network in a local prediction module;
step 4, extracting global scoring prediction;
step 4.1, according to the ith user u i Set of implicit social friends of H U (i) Obtaining the ith user u from HIN by using embedded layer i Potential feature vector p' i ∈R d' And potential influence vector x i ∈R d' And according to the recessive article relation set H of the a-th article V (a) Obtaining the a-th item v from the HIN by using the embedding layer a Potential feature vector q' a ∈R d' And potential influence vector y a ∈R d' ；
4.2, building a wide linear attention model consisting of a user global feature extraction module, an article global feature extraction module and a global prediction module;
step 4.2.1, the user global feature extraction module obtains the ith user u by using the formulas (5) to (8) based on three user single-hop neighbor paths i Global feature embedding vector of
In the formula (8), α ik E alpha represents the ith user u i For the k user u k Attention weight of (1), beta ij E beta represents the ith user u i For the jth user u in the recessive social friends ij Attention weight of (1), γ ia E gamma represents the ith user u i For the a-th article v a Attention weight of (1), R V (i) E R represents the ith user u i A set of items for which a score has been given; w is a group of 1 ，W 2 ，W 3 ，W 4 ，W 5 ，W 6 For six trainable parameter matrices, b 1 ，b 2 ，b 3 Three offset vectors; sigma is an activation function, and softmax represents a normalization function; t represents transposition;
step 4.2.2, the global feature of the article is extractedThe fetching module obtains the a-th item v by using the formula (9) a Global feature embedded vector of
In the formula (9), eta ab E eta represents the a-th item v a For the b-th recessive similar item v b Attention weight of (1);
step 4.2.3, mixing
Inputting the data into a global prediction module, and obtaining the ith user u by using an equation (10) i For the a-th item v a Is predicted based on the global score of->
In the formula (10), b i For the ith user u i User deviation of b a Is the a-th item v a μ is the mean value of all users after adding the scores of all the items;
step 5, obtaining the ith user u by using the formula (11) i For the a-th article v a Scoring the predicted outcome
In the formula (11), λ represents a scoring weight coefficient;
step 6, constructing a constraint aiming at the scoring behavior of the user;
step 6.1, defining a rating triple (U, R, V) of a user to an item, wherein U belongs to U and represents a user entity, V belongs to V and represents an item entity, R belongs to R and represents a rating relation, and calculating a feature vector through a TransH algorithm, wherein the method comprises the following steps: user entity feature vector e u Item entity feature vector e v Rank relational feature vector e r ；
Obtaining e according to formula (12) and formula (13) u ，e v Projection vector of user entity characteristic of hyperplane in relation r
And an item physical feature projection vector>
/>
In formulae (12) and (13), w r Is a normal vector corresponding to the hyperplane;
step 6.2, constructing a scoring function f (u, r, v) of the rating triple (u, r, v) by using the formula (14):
step 6.3, constructing a marginal loss function L by using the formula (15) KG ：
In the formula (16), [ f (-)] + Represents max (0, f (·)), V 'represents another item entity, (u, r, V') represents a false triple generated by V 'in place of V, V' is epsilon V; f (u, r, v ') represents the scoring function of the false triplet (u, r, v');
step 7, constructing a loss function L of the depth map model by using the formula (14) D ：
In formula (14), λ 1 ，λ 2 For regularization parameters, P represents a matrix formed by all user characteristic vectors, and Q represents a matrix formed by all article characteristic vectors;
construction of the loss function L of the Width Linear attention model Using equation (15) W ：
In formula (15), λ 3 For regularizing parameters, P 'represents a matrix formed by potential characteristic vectors of all users, Q' represents a matrix formed by potential characteristic vectors of all articles, X represents a matrix formed by potential influence vectors of all users, and Y represents a matrix formed by potential influences of all articles;
step 7, training the depth map model and the width linear attention model by using a gradient descent method respectively, and correspondingly calculating a loss function L D And a loss function L W Updating the model parameters until the loss function is converged, thereby obtaining a trained depth map model and a trained width linear attention model;
and 8, respectively inputting the article set, the user scoring matrix, the target user social friend set and the recessive social friend set of a certain target user into the trained depth map model and the trained width linear attention model, and correspondingly obtaining the local scoring prediction and the global scoring prediction of the target user, so that the scoring of the target user on the articles is calculated by using the formula (11), and K articles with the top scoring rank are selected and recommended to the target user, thereby completing the article recommendation of the target user.
2. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that enables the processor to perform the item recommendation method of claim 1, and wherein the processor is configured to execute the program stored in the memory.
3. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the item recommendation method according to claim 1.
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