CN115168653A - Cross-domain recommendation method and system based on inter-domain and intra-domain relationships - Google Patents

Cross-domain recommendation method and system based on inter-domain and intra-domain relationships Download PDF

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CN115168653A
CN115168653A CN202210547462.XA CN202210547462A CN115168653A CN 115168653 A CN115168653 A CN 115168653A CN 202210547462 A CN202210547462 A CN 202210547462A CN 115168653 A CN115168653 A CN 115168653A
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朱燕民
王科
唐飞龙
俞嘉地
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Abstract

A cross-domain recommendation method and a system based on inter-domain and intra-domain relations are disclosed, wherein cross-domain heteromorphic graphs are constructed and obtained according to the fact that comment information among users or articles in different domains is converted into semantic relations among the users or articles, and single-domain heteromorphic graphs are constructed and obtained by utilizing the semantic relations among the users or articles in each domain; and designing a cross-domain recommendation system model based on inter-domain and intra-domain relations on the basis of the two different composition graphs, wherein the cross-domain recommendation system model is used for synchronously learning the cross-domain invariance and single-domain specificity of the users or articles, and generating an individual recommendation result for each user through a gating fusion mechanism. According to the method, the semantic relation among the domains is converted into the cross-domain abnormal composition, the semantic relation in the domains is converted into the single-domain abnormal composition, meanwhile, the inter-domain and intra-domain relation systems are reserved, the high-order inter-domain and intra-domain relation is modeled uniformly, and the double-target cross-domain recommendation is realized.

Description

Cross-domain recommendation method and system based on inter-domain and intra-domain relationships
Technical Field
The invention relates to a technology in the field of information processing, in particular to a cross-domain recommendation method and a cross-domain recommendation system based on inter-domain and intra-domain relationships.
Background
The graph convolution technology mainly uses convolution technology to mine local information on a graph structure, and the core idea is to iteratively aggregate neighbor nodes through a propagation mechanism to update the characteristics of a target node. The graph convolution technique is mainly divided into two categories: user-item interaction graph convolution techniques and heterogeneous graph convolution techniques. For the former, the interaction data of the user and the item is converted into bipartite graph structure data, in which: the user and the item are considered nodes and the interaction between the user and the item is considered edges. However, this single user-item interaction only records the interaction between the user and the item, and ignores the other interactions that exist.
In order to explore the multi-type interaction relationship, researchers put forward modeling on social information of users, attribute information of articles, interaction sequence information and the like, and a heteromorphic graph is constructed. For example, a user-user interaction graph is constructed by using social information of a user, and researchers can explore social relationships of the user; an article-attribute interaction diagram is constructed by using a knowledge graph of an article, and researchers can explore the attribute relationship of the article; a user-user or article-article interaction graph is constructed by utilizing comment information of a user on an article, and researchers can explore semantic relations of the user or the article.
Disclosure of Invention
The invention provides a cross-domain recommendation method and a cross-domain recommendation system based on inter-domain and intra-domain relations, aiming at the defects that the existing cross-domain recommendation system only utilizes intra-domain relations and does not consider the relations between different users or objects between domains.
The invention is realized by the following technical scheme:
the invention relates to a cross-domain recommendation method based on inter-domain and intra-domain relations, which comprises the steps of converting comment information of users or articles in different domains into semantic relations of the users or the articles, constructing to obtain cross-domain heteromorphic graphs, and constructing to obtain single-domain heteromorphic graphs by utilizing the semantic relations of the users or the articles in each domain; and designing a cross-domain recommendation system model based on inter-domain and intra-domain relations on the basis of the two different composition graphs, wherein the cross-domain recommendation system model is used for synchronously learning the cross-domain invariance and single-domain specificity of the users or articles, and generating an individual recommendation result for each user through a gating fusion mechanism.
The invention relates to a system for realizing the method, which comprises the following steps: preprocessing unit, heterogeneous graph building unit, embedding layer, graph convolution layer, gate-controlled fusion layer and output layer, wherein: the preprocessing unit carries out primary processing on the original scoring and comment data to obtain input data; the heterogeneous graph building unit generates a relation matrix reflecting multi-dimensional relations among users, articles and users, and builds two single-field heterogeneous graphs and one cross-field heterogeneous graph on the basis of the relation matrix; the embedding layer converts the high-dimensional heterogeneous graph into a vector of a low-dimensional space; the graph convolution layer considers different types of relations between nodes, and utilizes the heterogeneous graph neural network technology to convert vectors into low-dimensional space for modeling so as to obtain relation-aware feature vectors; the gating fusion layer carries out deep fusion on the feature vectors and further extracts key features; and the output layer calculates the feature vector expression of the final user and the article according to the key features, and uses an output function as the interactive relation between the user and the article.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention.
Detailed Description
As shown in fig. 1, this embodiment relates to a cross-domain recommendation method based on inter-domain and intra-domain relationships, which includes:
step 1, data preprocessing: the method for conducting preliminary processing on the user and item interaction data including scoring and comment records, namely < user, item, raiting, review >, specifically comprises the following steps:
1.1 remove noise present in < user, item, raiting, review > interaction records, e.g. delete records with no score or comment, delete records with too short a comment;
1.2 aiming at the user and article interaction data in two different fields, selecting users in the two fields which are completely the same, and removing the interaction records of the different users between the two fields;
1.3 after data preprocessing, the user sets U in the two fields are completely the same, and the article sets I and J in the two fields are completely different.
Step 2, constructing a heterogeneous graph: given two fields a and B, the embodiment establishes a relationship and a relationship matrix by using the scoring and comment data of the user on the article, and constructs a heteromorphic graph on the basis of the relationship and the comment data, specifically including:
2.1, basic definition: the common user set between the two fields is U, the different item sets of the two fields are I and J respectively, and the number | U | = M of the common user set; the number of different articles in the areas A and B is I | = N A And | J | = N B Wherein: \8230Theuser-item relationships (including the user-item relationships of domains A and B) are established using scoring data, and all the user-item relationships in domain A form a matrix
Figure BDA0003649637590000021
All user-item relationships in Domain B form a matrix
Figure BDA0003649637590000022
User-user relationships (including cross-domain user-user relationships and single-domain user-user relationships) and item-item relationships (including cross-domain item-item relationships and single-domain item-item relationships) are established using the review data.
2.2 construction of Cross-DomainPatterning by different patterns: first, user-user relationships between domains are established. For a certain user u, all comments of the user are connected to generate a user document d u . Converting text documents to fixed-size vector representation D using a BERT model u (ii) a Probability of user-user relation obtained by calculating cosine similarity
Figure BDA0003649637590000023
Wherein: ψ (x) = max (0, x) is a ReLU function; calculating cosine similarity between all different users in different fields to obtain a cross-field user-user relation matrix
Figure BDA0003649637590000024
Similarly, a cross-domain item-item relationship matrix may be obtained
Figure BDA0003649637590000025
Figure BDA0003649637590000031
Composition domain A user-item relationship composition matrix R A Domain B user-item relationships form a matrix R B Cross-domain user-user relationship matrix C U Cross domain item-item relationship matrix
Figure BDA0003649637590000032
A cross-domain metamorphic pattern can be formed, namely:
Figure BDA0003649637590000033
wherein:
Figure BDA0003649637590000034
Figure BDA0003649637590000035
are each R A ，R B ，C I The transposed matrix of (2).
2.3, constructing a single-field abnormal graph: obtaining a domain A user-user relationship matrix by establishing user-user relationships in domain A
Figure BDA0003649637590000036
By establishing an item-item relationship in Domain A, a Domain A item-item relationship matrix may be obtained
Figure BDA0003649637590000037
Finally, a domain a differential map is constructed:
Figure BDA0003649637590000038
wherein:
Figure BDA0003649637590000039
similarly, a domain B differential map is constructed:
Figure BDA00036496375900000310
wherein:
Figure BDA00036496375900000311
2.4, relation definition: in the isomerous graph G = G C ∪G A ∪G B In (1), there are eight different relationships, for a particular relationship
Figure BDA00036496375900000312
r =1222 823028, wherein:
Figure BDA00036496375900000313
for the user-user relationship between the domains,
Figure BDA00036496375900000314
for inter-domain item-to-item relationships,
Figure BDA00036496375900000315
for the domain a user-user relationship,
Figure BDA00036496375900000316
for the domain B user-user relationship,
Figure BDA00036496375900000317
for the domain a article-to-article relationship,
Figure BDA00036496375900000318
for the domain B article-to-article relationship,
Figure BDA00036496375900000319
for domain a user-item relationships,
Figure BDA00036496375900000320
is a domain B user-item relationship. In particular, it is possible to use, for example,
Figure BDA00036496375900000321
Figure BDA00036496375900000322
step 3, constructing a cross-domain recommendation model based on inter-domain and intra-domain relations according to the two heterogeneous graphs obtained in the step 2, learning a relation-aware vector expression for each user or article, and fusing cross-domain invariance and single-domain specificity through a gating fusion mechanism, wherein the model comprises the following steps: embedding layer, relation perception map convolution layer, gate fusion layer and output layer, wherein: the embedding layer adopts a one-hot vector X as a common user U, a field A article I and a field B article J, and converts the high-dimensional one-hot vector into low-dimensional expression; the relation sensing graph convolution layer considers the relations of different types among the nodes, simultaneously considers the relation types among the nodes and captures the heterogeneous connectivity between users and object objects in different graphs when aggregating the neighbor nodes of a target node, and then splices the vector expressions of different layers to form the final vector expression; gate-controlled fusion layer generating cross-domain invariance of users and items on cross-domain heteromorphic graphs using neural network models: (
Figure BDA00036496375900000323
And
Figure BDA00036496375900000324
) And single domain specificity: (
Figure BDA00036496375900000325
And
Figure BDA00036496375900000326
) Two gate control units sharing part of parameters are utilized to realize a dual-domain target recommendation task; output layer generating vector representations for end users
Figure BDA00036496375900000327
And
Figure BDA00036496375900000328
and vector representation of the final article
Figure BDA00036496375900000329
And
Figure BDA00036496375900000330
the predicted probability between user u and item i or j is:
Figure BDA00036496375900000331
Figure BDA00036496375900000332
3.1, setting an embedding layer: converting the high-dimensional one-hot vector into low-dimensional expression, specifically comprising the following steps: h = XP, wherein:
Figure BDA00036496375900000333
is a transformation matrix, d 1 ＜＜d 0 . For each row h of the output matrix, there is a low-dimensional representation of a node (user or article), where:
Figure BDA00036496375900000334
respectively a certain user u in a heterogeneous graph G C 、G A And G B The expression of the protein in the medium and low dimensions,
Figure BDA00036496375900000335
respectively, a region A of a certain user i in a heterogeneous graph G C And G A The expression of the medium and low dimension is realized,
Figure BDA00036496375900000336
Figure BDA0003649637590000041
respectively an object j in the field B in an isomeric diagram G c And G B Low dimensional expression of (b), h r Is in a relationship of
Figure BDA0003649637590000042
Low dimensional expression of (2).
3.2, relation perception graph convolution layer setting: in a graph convolution propagation mechanism, a vector expression of a relationship type is added to distinguish the heterogeneity of the relationship between different nodes, and the relationship perception of a user u for a heterogeneous graph convolution layer is specifically as follows:
Figure BDA0003649637590000043
Figure BDA0003649637590000044
wherein:
Figure BDA0003649637590000045
and
Figure BDA0003649637590000046
are respectively node v r And the vector expression of the relation r after the k-th layer, sigma is a nonlinear activation function, W (k) As a weight matrix, N u And
Figure BDA0003649637590000047
respectively node u and node v r Of the node(s) of (a) is,
Figure BDA0003649637590000048
is a regularization constant term; vector representation of relationship types
Figure BDA0003649637590000049
The updating method specifically comprises the following steps:
Figure BDA00036496375900000410
wherein:
Figure BDA00036496375900000411
the relationship and node vectors are mapped to the same space as the weight matrix. Note that H is defined as the initial vector representation of the graph-convolution network, i.e., E (0) H, in particular:
Figure BDA00036496375900000412
the heterogeneous graph convolution layer may also describe a relationship-aware heterogeneous graph convolution in a matrix form, specifically:
Figure BDA00036496375900000413
Figure BDA00036496375900000414
wherein:
Figure BDA00036496375900000415
and
Figure BDA00036496375900000416
are respectively node v r And the vector representation of the relation r after the k-th layer.
Figure BDA00036496375900000417
Is a symmetric matrix normalization matrix, D is a diagonal matrix, and G is an adjacency matrix of the heteromorphic graph. Finally, the vector expressions of the different layers are spliced to form the final vector expression:
Figure BDA00036496375900000418
wherein: and | is a join operation.
Figure BDA00036496375900000419
For the final vector representation after the heterogeneous graph convolution operation, d 2 The size of the vector after the K-layer propagation and join operations is defined. By usingAfter the relation-aware heterogeneous graph convolution operation and the splicing operation, the vector expression of the user on different heterogeneous graphs can be output, namely G C Vector representation of upper user u
Figure BDA00036496375900000420
At G A Vector representation of upper user u
Figure BDA00036496375900000421
At G B Vector representation of upper user u
Figure BDA00036496375900000422
Similarly, can be found in G C Vector representation of item i
Figure BDA00036496375900000423
At G A Vector representation of item i
Figure BDA00036496375900000424
At G C Vector representation of item j
Figure BDA00036496375900000425
At G B Vector representation of item j
Figure BDA00036496375900000426
3.3, setting of a gated fusion layer: two gating cells sharing part of the parameters were designed to fuse these two properties:
Figure BDA00036496375900000427
Figure BDA00036496375900000428
Figure BDA00036496375900000429
wherein:
Figure BDA00036496375900000430
the laminated layer being two doorsThe control units share a weight matrix and,
Figure BDA00036496375900000431
and
Figure BDA00036496375900000432
is a matrix of weights for each of the two gating cells,
Figure BDA00036496375900000433
for the fused vector representation of user u in domain a,
Figure BDA00036496375900000434
fusion vector expression of user u in the field B; a universal gate control unit is adopted to fuse the cross-domain invariance and the single-domain specificity of the A-domain article i, and specifically the following steps are adopted:
Figure BDA00036496375900000435
wherein:
Figure BDA00036496375900000436
and
Figure BDA00036496375900000437
is the respective weight matrix 2 of the two gating cells
Figure BDA00036496375900000438
Table field a fusion vector representation of item i. Similar to
Figure BDA00036496375900000439
The generation process of (2) can obtain the fusion vector expression of the cross-domain invariance and the single-domain specificity of the domain B article j
Figure BDA00036496375900000440
3.4, the output layer refers to: in order to explore the nonlinear high-order characteristics of user vector expression and article vector expression, the output layer adopts a multilayer perceptron (MLP) to model the interaction information of the user and the article, and the method specifically comprises the following steps:
Figure BDA00036496375900000441
Figure BDA00036496375900000442
φ L ＝a L (S L φ L-1 +b L )，
Figure BDA00036496375900000443
wherein: s l And b l Weight matrix and bias term, a, of the l-th layer, respectively l For activating the function, f (-) is the mapping function, and L mapping to predicted values for Domain A
Figure BDA0003649637590000051
Similarly, a prediction value for Domain B may be generated
Figure BDA0003649637590000052
Step 4, model training, specifically comprising: using cross entropy loss function
Figure BDA0003649637590000053
Figure BDA0003649637590000054
Wherein:
Figure BDA0003649637590000055
and
Figure BDA0003649637590000056
loss functions for domain a and domain B respectively,
Figure BDA0003649637590000057
the actual interactive data of the user-article can be used as a positive sample training set; for each positive sample, a plurality of non-interacted articles are sampled to be used as negative samples to form a negative sample training set
Figure BDA0003649637590000058
Since the objective of this embodiment is to optimize the recommended tasks of both domains as accurately as possible, the joint loss function is needed to be used when training the system
Figure BDA0003649637590000059
And
Figure BDA00036496375900000510
in combination, the joint loss function is:
Figure BDA00036496375900000511
Figure BDA00036496375900000512
wherein:
Figure BDA00036496375900000513
is a regularization term.
Step 5, generating a recommendation result: after the model is trained in a gradient descent mode, the prediction probability between all the items of the two fields is calculated for each user
Figure BDA00036496375900000514
And
Figure BDA00036496375900000515
and then according to the estimated probability, sequencing the articles, wherein the articles with the sequencing result in front can be used as the result of personalized recommendation for the user, so that a task of recommending in two fields (dual-target recommendation) is realized.
To verify the model effect, the present embodiment establishes three cross-domain recommended tasks on amazon data sets to verify the effect of the present embodiment, which specifically includes:
5.1 detailed parameter settings during the experiment: selecting overlapped users from each pair of cross-domain data sets and filtering out non-overlapped users; the experiment was then performed using a tensflow framework. When a training set is constructed, four negative samples are randomly collected while each positive sample is selected. In the model training phase, adam is selected as an optimizer to update model parameters, and the learning rate is set to be 0.001. The amount of training data for each batch is set to 512 and the user and item embedding vector dimension is set to 32. Furthermore, using the dropout technique prevents the overfitting problem and sets the dropout size to 0.1; the number of layers of the final graph convolution is uniformly set to 4.
5.2 evaluation method. The embodiment adopts a cross-validation method (leave-one-out method) for evaluation, and compares the evaluation with the existing single-field method, the cross-field method based on the multilayer perceptron and the cross-field method based on the graph neural network. When the effect of the cross-domain recommendation system is verified, the Hit rate (abbreviated as HR) and the Normalized distributed additive discount Gain (abbreviated as NDCG) are used as evaluation indexes, and the performance of the model above HR @10 and NDCG @10 is specifically given. For each user, 99 items that have not interacted with the user are randomly drawn and combined with the positive sample that has interacted with the user to form a ranked candidate list in a ranking process.
5.3 evaluation results. Compared with the prior related work, the method is improved by 12.16 percent and 18.15 percent on average in three cross-domain recommended tasks.
The foregoing embodiments may be modified in many different ways by one skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and not by the preceding embodiments, and all embodiments within their scope are intended to be limited by the scope of the invention.

Claims (4)

1. A cross-domain recommendation method based on inter-domain and intra-domain relationships is characterized in that cross-domain heteromorphic graphs are constructed and obtained according to the fact that comment information among users or articles in different domains is converted into semantic relationships among the users or articles, and single-domain heteromorphic graphs are constructed and obtained by the aid of the semantic relationships among the users or articles in each domain; and designing a cross-domain recommendation system model based on inter-domain and intra-domain relations on the basis of the two different composition graphs, wherein the cross-domain invariance and single-domain particularity of the users or the articles are synchronously learned, and personalized recommendation results for each user are generated through a gating fusion mechanism.
2. The inter-domain and intra-domain relationship-based cross-domain recommendation method of claim 1, specifically comprising:
step 1, data preprocessing: the data of the interaction between the user and the goods, including the scoring and commenting records, namely < user, item, raiting, review >, is primarily processed,
step 2, constructing an isomeric diagram: given two fields A and B, the embodiment establishes a relationship and a relationship matrix by using the scoring and comment data of a user on an article, and constructs a heteromorphic graph on the basis of the relationship and the relationship matrix;
step 3, constructing a cross-domain recommendation model based on inter-domain and intra-domain relations according to the two heterogeneous graphs obtained in the step 2, learning a relation-aware vector expression for each user or article, fusing cross-domain invariance and single-domain specificity through a gating fusion mechanism,
step 4, training a cross-domain recommendation model based on inter-domain and intra-domain relations by adopting a cross entropy loss function;
and 5, calculating the prediction probability between each user and all the articles in the two fields through the trained model, sequencing the articles based on the prediction probabilities, and obtaining the personalized recommendation result for the user, thereby realizing the dual-target recommendation in the two fields.
3. The inter-domain and intra-domain relationship-based cross-domain recommendation method of claim 1, wherein the inter-domain and intra-domain relationship-based cross-domain recommendation model comprises: embedding layer, relation perception map convolution layer, gate-controlled fusion layer and output layer, wherein: the embedding layer adopts a one-hot vector X as a common user U, a field A article I and a field B article J, and converts the high-dimensional one-hot vector into low-dimensional expression; the relation perception graph convolution layer considers different types of relations between nodes and aggregates neighbor nodes of a target nodeMeanwhile, the relation types among the nodes are considered, heterogeneous connectivity between users and object objects in different graphs is captured, and then vector expressions of different layers are spliced to form final vector expressions; gate-controlled fusion layer generating cross-domain invariance of users and items on cross-domain heteromorphic graphs using neural network models: (
Figure FDA0003649637580000011
And
Figure FDA0003649637580000012
) And single domain specificity: (
Figure FDA0003649637580000013
Figure FDA0003649637580000014
And
Figure FDA0003649637580000015
) Two gate control units sharing part of parameters are utilized to realize a dual-domain target recommendation task; output layer generating vector representations for end users
Figure FDA0003649637580000016
And
Figure FDA0003649637580000017
and vector representation of the final article
Figure FDA0003649637580000018
And
Figure FDA0003649637580000019
the predicted probability between user u and item i or j is:
Figure FDA0003649637580000021
4. a cross-domain recommendation system based on inter-domain and intra-domain relationships implementing the method of any one of claims 1 to 3, comprising: preprocessing unit, heterogeneous graph building unit, embedding layer, graph convolution layer, gate-controlled fusion layer and output layer, wherein: the preprocessing unit carries out primary processing on the original scoring and comment data to obtain input data; the heterogeneous graph building unit generates a relation matrix reflecting multi-dimensional relations among users, articles and users, and builds two single-field heterogeneous graphs and one cross-field heterogeneous graph on the basis of the relation matrix; the embedding layer converts the high-dimensional heterogeneous graph into a vector of a low-dimensional space; the graph convolution layer considers the relations of different types among the nodes, and utilizes the heterogeneous graph neural network technology to convert the vectors into the vectors of the low-dimensional space for modeling so as to obtain the feature vectors of relation perception; the gating fusion layer carries out deep fusion on the feature vectors and further extracts key features; and the output layer calculates the feature vector expression of the final user and the article according to the key features, and uses an output function as the interactive relation between the user and the article.
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CN117648493A (en) * 2023-12-13 2024-03-05 南京航空航天大学 Cross-domain recommendation method based on graph learning

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* Cited by examiner, † Cited by third party
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CN117648493A (en) * 2023-12-13 2024-03-05 南京航空航天大学 Cross-domain recommendation method based on graph learning
CN117648493B (en) * 2023-12-13 2024-05-31 南京航空航天大学 Cross-domain recommendation method based on graph learning

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