CN114880583A - Cross-domain social recommendation method based on self-supervision learning - Google Patents

Cross-domain social recommendation method based on self-supervision learning Download PDF

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CN114880583A
CN114880583A CN202210511621.0A CN202210511621A CN114880583A CN 114880583 A CN114880583 A CN 114880583A CN 202210511621 A CN202210511621 A CN 202210511621A CN 114880583 A CN114880583 A CN 114880583A
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吴乐
罗苏常
杨永晖
张琨
孙培杰
洪日昌
张志强
周俊
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Hefei University of Technology
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Abstract

The invention discloses a cross-domain social product recommendation method based on self-supervision learning, which comprises the following steps: 1. constructing heterogeneous data: a scoring matrix of the product by the user and a social relationship matrix between the user and the user; 2. processing the heterogeneous data through a heterogeneous graph construction layer to obtain a heterogeneous graph network of a user-product; 3. obtaining a user cooperation matrix and a product cooperation matrix through a single hot coding layer; 4. performing feature propagation through the graph convolution layer; 5. constructing a node feature aggregation layer; 6. constructing a node prediction layer to recommend products; 7. and constructing a self-supervision learning layer to carry out mutual information maximization learning on the local characteristics and the global characteristics of the nodes until the product recommendation effect of the social cold start user in the social domain is optimal. The method can fully mine the global information of the whole abnormal graph, reduce the dependence of the model on the number of bridge users and learn better bridge user characteristics, thereby improving the product recommendation performance on social cold-start users.

Description

Cross-domain social recommendation method based on self-supervision learning
Technical Field
The invention relates to the field of cross-domain recommendation and social recommendation, in particular to a cross-domain social recommendation method based on self-supervision learning.
Background
Social recommendation effectively alleviates the problems of data sparsity, system cold start and the like. According to social homogeneity theory, users tend to buy products recommended by their friends rather, and there are often more similar interest preferences among friends. Therefore, compared with the traditional collaborative filtering recommendation algorithm, the social recommendation method has more information so as to more effectively model the interest preference of the user and improve the recommendation performance of the recommendation system. However, current social recommendation methods all have a strict constraint that a user must have a social relationship based on historical product interaction records, so that the user can learn the potential interest and preference of the user, which is not in accordance with a real application scenario. In a real scenario, most users often have only social relationships without historical product interaction records, and we define this group as a social cold-start user. Therefore, how to recommend products to a large number of social cold-start users becomes a difficult problem which needs to be solved urgently at present.
One possible approach is to use the idea of cross-domain recommendation to address the problem of product recommendation to a large number of social cold-start users. The main idea of cross-domain recommendation is to use a source domain with richer information content as an auxiliary domain and transfer valuable information to a target domain, so as to improve the performance of a recommendation system on the target domain. However, the current cross-domain recommendation methods are not suitable for the proposed cross-domain social recommendation scenario, because the methods depend on the number of bridge users to a large extent, which may cause that when the number of bridge users is small, the model becomes fragile, and the recommendation effect on the social cold-start user is not accurate. Therefore, how to capture more information from a very limited bridge user is the focus of the present invention.
Disclosure of Invention
The invention provides a cross-domain social recommendation method based on self-supervision learning, aiming at overcoming the defects of the prior art, so that the global information of the whole abnormal graph can be fully mined, the dependence of a model on the number of bridge users is reduced, and the bridge user representation with better learning is realized, thereby improving the product recommendation performance on social cold-start users.
The invention adopts the following technical scheme for solving the technical problems:
the invention relates to a cross-domain social product recommendation method based on self-supervision learning, which is characterized by comprising the following steps of:
step 1, constructing heterogeneous data, comprising: a scoring matrix R of the product by the user and a social relationship matrix S between the users are as follows:
let U I Represents a set of users over an information domain, and
Figure BDA0003638207970000011
wherein the content of the first and second substances,
Figure BDA0003638207970000012
indicating the a-th user in the information field,
Figure BDA0003638207970000013
representing the b-th user in the information domain, M represents the total number of users in the information domain, a is more than or equal to 1, and b is more than or equal to M;
let V denote the product set on the information field, and V ═ V 1 ,…v i ,…v j ,…v L In which v is i Indicating the ith product in the information field, v j Indicating the jth product in the information field, L indicating the letterTotal number of products on the domain;
let R ai Representing the a-th user
Figure BDA0003638207970000021
For the ith product v i The scoring value of (2) is that the scoring matrix of the product by the user on the information domain is R ═ { R ═ R ai } M×L ；
Let U S Represents a set of users on a social domain,
Figure BDA0003638207970000022
wherein the content of the first and second substances,
Figure BDA0003638207970000023
representing the c-th user in the social domain,
Figure BDA0003638207970000024
representing the d-th user in the social domain, N representing the total number of users in the social domain, c being more than or equal to 1, and d being more than or equal to N;
order S cd Represents the c-th user
Figure BDA0003638207970000025
For the d-th user
Figure BDA0003638207970000026
If there is a connection between two users, let S cd If not, let S cd 0, the social relationship matrix between users in the social domain is S ═ S cd } N×N ；
Step 2, utilizing a bridge user set U coexisting in an information domain and a social domain B ＝U I ∩U S Constructing a heterogeneous graph network G ═ G I ∪G S Wherein G is I Representing the set U of all users in the information domain I Graph network Structure with product set V, G S Representing a set U of all users in a social domain S A graph network structure for collecting U-shaped bridge users B The total number of users in (1) is T;
and 3, obtaining a node cooperation matrix through single hot coding, wherein the method comprises the following steps: user cooperation matrix, product cooperation matrix:
step 3.1, constructing a user cooperation matrix P ═ P in a single hot coding mode for all users in the heterogeneous graph network G 1 ,…,p a ,…,p M-T ,…,p b ,…,p c ,…,p M ,…,p d ,…,p M+N-T And P ∈ R (M+N-T)×d Wherein p is a Representing a d-dimensional cooperation vector of an a-th user in the user cooperation matrix P, wherein the a-th user belongs to a non-bridge user in the information domain; p is a radical of b Representing the d-dimensional cooperation vector of the b-th user in the user cooperation matrix P, wherein the b-th user belongs to the bridge user, P d Representing a d-dimensional cooperation vector of a d-th user in the user cooperation matrix P, wherein the d-th user belongs to a non-bridge user in the social domain;
step 3.2, constructing a product cooperation matrix Q ═ Q in a single-hot coding mode for all products in the heteromorphic graph network G 1 ,…,q i ,…q L And Q ∈ R L×d Wherein q is i A d-dimensional collaborative vector representing an ith product;
and 4, carrying out characteristic propagation through the graph volume layer:
step 4.1, making the graph convolution layers have K convolution layers, and initializing the number K of the current convolution layers to be 0;
step 4.2, obtaining the d-dimensional cooperation vector p of the a-th non-bridge user in the information domain according to the formula (1), the formula (2) and the formula (3) respectively a D-dimensional cooperation vector p of the b-th bridge user b D-dimensional cooperation vector p of the d-th non-bridge user in social domain d After passing through the (k + 1) th graph convolution layer, the corresponding user characteristic vector is obtained
Figure BDA0003638207970000027
Obtaining a d-dimensional cooperative vector q of the ith product according to the formula (4) i The product characteristic vector obtained after passing through the (k + 1) th graph convolution layer
Figure BDA0003638207970000028
Figure BDA0003638207970000029
Figure BDA00036382079700000210
Figure BDA00036382079700000211
Figure BDA0003638207970000031
In the formula (1), R a Representing the set of products that the a-th non-bridge user interacted with,
Figure BDA0003638207970000032
is the d-dimensional cooperation vector p of the a-th non-bridge user in the information domain a When k is 0, let the user characteristic vector output from the k-th convolution layer
Figure BDA0003638207970000033
A user feature vector is initialized randomly;
in the formula (2), R b Representing the set of products, S, interacted by the b-th bridge user b Representing a set of users with social connections to the b-th bridge user,
Figure BDA0003638207970000034
is d-dimensional cooperation vector p of the b-th bridge user b When the user characteristic vector output by the k-th convolutional layer is equal to 0,
Figure BDA0003638207970000035
a user feature vector is initialized randomly;
in the formula (3), S d Representing a set of users for which the d-th non-bridge user has social connections,
Figure BDA0003638207970000036
the user feature vector is output by the d-th non-bridge user in the social domain at the kth layer convolutional layer, when k is 0,
Figure BDA0003638207970000037
a user feature vector is initialized randomly;
in the formula (4), R i Representing a set of users who have interacted with the ith product,
Figure BDA0003638207970000038
is the d-dimensional cooperation vector q of the ith product i When the characteristic vector of the product output by the k-th layer convolution layer is 0,
Figure BDA0003638207970000039
the feature vector of the product is initialized randomly;
4.3, assigning K +1 to K, judging whether K is greater than K, and if so, executing the step 5; otherwise, returning to the step 4.2 for sequential execution;
step 5, constructing a node feature aggregation layer:
performing feature aggregation on the K-layer user feature vector and the product feature vector according to the formula (5) to obtain a feature vector p of any user u in the heterogeneous network G u And the feature vector q of the ith product i ：
Figure BDA00036382079700000310
In the formula (5), AVG represents the average pooling operation,
Figure BDA00036382079700000311
a feature vector representing the user u output by the kth convolutional layer,
Figure BDA00036382079700000312
i-th product q representing the output of the k-th convolutional layer i The feature vector of (2);
step 6, constructing a node prediction layer for product recommendation:
step 6.1, calculating the prediction score value of the user u on the ith product according to the formula (6)
Figure BDA00036382079700000313
Thereby obtaining a prediction scoring matrix of the product by the user
Figure BDA00036382079700000314
Figure BDA00036382079700000315
In the formula (6), <, > represents a vector inner product operation;
step 6.2, establishing a supervision loss function L according to the formula (7) R ：
Figure BDA00036382079700000316
In formula (7), σ (z) is a nonlinear activation function, and θ ═ P, Q]Is the parameter to be optimized,. psi. I ＝{(u,i,j)|u∈U I ,i∈R u ,j∈(V-R u ) Is the training data of all users on the information domain; r u Representing a set of products representing the u-th user interaction;
Figure BDA00036382079700000414
representing the predicted value of credit of the jth product to the jth user;
and 7, constructing an automatic supervision learning layer to carry out mutual information maximization learning on the local characteristics and the global characteristics of the nodes:
step 7.1, constructing the d user in the social domain according to the formula (8) and the formula (9)
Figure BDA0003638207970000041
Local feature h of d And the a-th user in the information domain
Figure BDA0003638207970000042
Local feature h of ai ：
h d ＝σ(p d ) (8)
h ai ＝[σ(p a )，σ(q i )] (9)
In the formula (8), sigma is sigmoid nonlinear activation function;
in the formula (9), [, ] is the splicing operation of the vectors;
step 7.2, constructing global features g of all users in the social domain according to the formula (10) and the formula (11) S And global characteristics g of all users in the information domain I ：
Figure BDA0003638207970000043
Figure BDA0003638207970000044
Step 7.3, constructing a discriminator D on the social domain according to the formula (12) and the formula (13) S And a discriminator D on the information field I ：
Figure BDA0003638207970000045
Figure BDA0003638207970000046
In the formula (12), h S Local features expressed as users on the social domain; w S ∈R d×d Is a trainable weight matrix, (h) S ,g S ) Is a positive sample pair on the social domain;
in the formula (13), h I Local features expressed as users on the information domain; w I ∈R 2d×2d Is a trainable weight matrix, (h) I ,g I ) Is a positive sample pair over the information field;
and 7.4, randomly adding and deleting the same number of edges of the heterogeneous graph network G to form a disturbing graph
Figure BDA0003638207970000047
And obtaining local features on the fictitious social domain according to the processes of the step 4, the step 5 and the step 7.1
Figure BDA0003638207970000048
And local features on fictitious information domains
Figure BDA0003638207970000049
Thus forming negative sample pairs on the social domain
Figure BDA00036382079700000410
And negative example pairs on the information field
Figure BDA00036382079700000411
Step 7.5, constructing a social domain self-supervision loss function L according to the formula (14) and the formula (15) S And information Domain autonomous loss function L I ：
Figure BDA00036382079700000412
Figure BDA00036382079700000413
And 7.5, constructing a total loss function L according to the formula (16):
L＝L R +αL s +λL I (16)
in the formula (16), α and λ are hyper-parameters for balancing two kinds of the auto-supervised loss functions;
7.6, solving all the loss functions L by using a gradient descent method to ensure that L converges to a minimum value, thereby obtaining an optimal parameter theta * ＝[P * ,Q * ]Further obtain the optimal user pre-predictionEvaluation matrix
Figure BDA0003638207970000054
And predicting a scoring matrix according to the optimal user
Figure BDA0003638207970000055
And recommending the product to the non-bridge user in the social domain.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a cross-domain social recommendation method based on self-supervision, which aims at social cold start users, utilizes bridge users to construct a heterogeneous graph network of an information domain and a social domain, learns potential cooperative information between the social cold start users and products through graph convolution operation, and utilizes a self-supervision learning method to construct supervision signals of nodes in a heterogeneous graph, fully excavates global characteristics of the heterogeneous graph, effectively improves the representation precision of the users and the products, and improves the recommendation precision on the social cold start users.
2. The invention processes the user set and the product set in a single hot coding mode, can effectively expand data dimensionality and is convenient for fast matrix operation.
3. The method and the system utilize the bridge user to construct the information domain and the heteromorphic network on the social domain to construct the connection between the social cold start user and the product, thereby relieving the problem of social cold start.
4. The invention adopts an automatic supervision optimization method, captures more overall information of the heterogeneous graph in a mode of maximizing the mutual information of local characteristics and overall characteristics of user nodes in the heterogeneous graph, and improves the node representation precision.
Drawings
FIG. 1 is a flowchart of a cross-domain social recommendation method based on self-supervised learning according to the present invention.
Detailed Description
In the embodiment, the cross-domain social recommendation method based on the self-supervision learning considers the problem that the number of bridge users is excessively depended on in cross-domain recommendation, establishes the connection between the social cold-starting users and products by establishing a heterogeneous graph network, realizes the mutual information maximization of the local features and the global features of heterogeneous graph nodes by an self-supervision optimization method, and captures more global features of heterogeneous graphs so as to realize more accurate product recommendation of the social cold-starting users. Specifically, as shown in fig. 1, the method comprises the following steps:
step 1, constructing heterogeneous data, comprising: a scoring matrix R of the product by the user and a social relationship matrix S between the users are as follows:
let U I Represents a set of users over an information domain, and
Figure BDA0003638207970000051
wherein the content of the first and second substances,
Figure BDA0003638207970000052
indicating the a-th user in the information field,
Figure BDA0003638207970000053
representing the b-th user in the information domain, M represents the total number of users in the information domain, a is more than or equal to 1, and b is more than or equal to M;
let V denote the product set on the information field, and V ═ V 1 ,…v i ,…v j ,…v L In which v is i Indicating the ith product in the information field, v j Indicating the jth product in the information field, and L indicating the total number of products in the information field;
let R ai Representing the a-th user
Figure BDA0003638207970000061
For the ith product v i The scoring value of (2) is that the scoring matrix of the product by the user on the information domain is R ═ { R ═ R ai } M×L ；
Let U S Represents a set of users on a social domain,
Figure BDA0003638207970000062
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003638207970000063
representing the c-th user in the social domain,
Figure BDA0003638207970000064
representing the d-th user in the social domain, N representing the total number of users in the social domain, c being more than or equal to 1, and d being more than or equal to N;
order S cd Represents the c-th user
Figure BDA0003638207970000065
For the d-th user
Figure BDA0003638207970000066
If there is a connection between two users, let S cd If not, let S cd 0, the social relationship matrix between users in the social domain is S ═ S cd } N×N ；
Step 2, utilizing a bridge user set U coexisting in an information domain and a social domain B ＝U I ∩U S Constructing a heterogeneous graph network G ═ G I ∪G S Wherein G is I Representing the set U of all users in the information domain I Graph network structure, G, with product set V S Representing a set U of all users in a social domain S A graph network structure for collecting U-shaped bridge users B The total number of users in (1) is T;
and 3, obtaining a node cooperation matrix through single hot coding, wherein the method comprises the following steps: user cooperation matrix, product cooperation matrix:
step 3.1, constructing a user cooperation matrix P ═ P in a single hot coding mode for all users in the heterogeneous graph network G 1 ,…,p a ,…,p M-T ,…,p b ,…,p c ,…,p M ,…,p d ,…,p M+N-T And P ∈ R (M+N-T)×d Wherein p is a Representing a d-dimensional cooperation vector of an a-th user in the user cooperation matrix P, wherein the user belongs to a non-bridge user on the information domain; p is a radical of b Representing the d-dimensional cooperation vector of the b-th user in the cooperation matrix P of the users belonging to the bridge user, P d Representing the d-dimensional cooperation vector of the d-th user in the user cooperation matrix P, wherein the user belongs to the social domainNon-bridge users;
step 3.2, constructing a product cooperation matrix Q ═ Q in a single-hot coding mode for all products in the heteromorphic graph network G 1 ,…,q i ,…q L And Q ∈ R L×d Wherein q is i A d-dimensional collaborative vector representing an ith product;
and 4, carrying out characteristic propagation through the graph volume layer:
step 4.1, making the graph convolution layers have K convolution layers, and initializing the number K of the current convolution layers to be 0;
step 4.2, obtaining the d-dimensional cooperation vector p of the a-th non-bridge user in the information domain according to the formula (1), the formula (2) and the formula (3) respectively a D-dimensional cooperation vector p of the b-th bridge user b D-dimensional cooperation vector p of the d-th non-bridge user in social domain d After passing through the (k + 1) th graph convolution layer, the corresponding user characteristic vector is obtained
Figure BDA0003638207970000067
Obtaining a d-dimensional cooperative vector q of the ith product according to the formula (4) i The product characteristic vector obtained after passing through the (k + 1) th graph convolution layer
Figure BDA0003638207970000068
Figure BDA0003638207970000069
Figure BDA00036382079700000610
Figure BDA00036382079700000611
Figure BDA00036382079700000612
In the formula (1), R a Representing the collection of products that the a-th non-bridge user has interacted with,
Figure BDA0003638207970000071
is the d-dimensional cooperation vector p of the a-th non-bridge user in the information domain a When k is 0, let the user characteristic vector output from the k-th convolution layer
Figure BDA0003638207970000072
A user feature vector is initialized randomly;
in the formula (2), R b Representing the set of products, S, interacted by the b-th bridge user b Representing a set of users with social connections to the b-th bridge user,
Figure BDA0003638207970000073
is d-dimensional cooperation vector p of the b-th bridge user b When the user characteristic vector output by the k-th convolutional layer is equal to 0,
Figure BDA0003638207970000074
a user feature vector is initialized randomly;
in formula (3), S d Representing a set of users for which the d-th non-bridge user has social connections,
Figure BDA0003638207970000075
the user feature vector is output by the d-th non-bridge user in the social domain at the kth layer convolutional layer, when k is 0,
Figure BDA0003638207970000076
a user feature vector is initialized randomly;
in the formula (4), R i Representing a set of users who have interacted with the ith product,
Figure BDA0003638207970000077
is the d-dimensional cooperation vector q of the ith product i When the characteristic vector of the product output by the k-th layer convolution layer is 0,
Figure BDA0003638207970000078
the feature vector of the product is initialized randomly;
4.3, assigning K +1 to K, judging whether K is greater than K, and if so, executing the step 5; otherwise, returning to the step 4.2 for sequential execution;
step 5, constructing a node feature aggregation layer:
performing feature aggregation on the K-layer user feature vector and the product feature vector according to the formula (5) to obtain a feature vector p of any user u in the heterogeneous network G u And the feature vector q of the ith product i ：
Figure BDA0003638207970000079
In the formula (5), AVG represents the average pooling operation,
Figure BDA00036382079700000710
a feature vector representing the user u output by the kth convolutional layer,
Figure BDA00036382079700000711
i-th product q representing the output of the k-th convolutional layer i The feature vector of (2);
step 6, constructing a node prediction layer for product recommendation:
step 6.1, calculating the prediction scoring value of the user u on the ith product according to the formula (6)
Figure BDA00036382079700000712
Thereby obtaining a prediction scoring matrix of the product by the user
Figure BDA00036382079700000713
Figure BDA00036382079700000714
In the formula (6), <, > represents a vector inner product operation;
step 6.2, establishing a supervision loss function L according to the formula (7) R ：
Figure BDA00036382079700000715
In formula (7), σ (z) is a nonlinear activation function, and θ ═ P, Q]Is the parameter to be optimized,. psi. I ＝{(u,i,j)|u∈U I ,i∈R u ,j∈(V-R u ) The training data of all users in the information domain; r u Representing a set of products representing the u-th user interaction;
Figure BDA00036382079700000716
representing the predicted value of credit of the jth product to the jth user;
and 7, constructing an automatic supervision learning layer to carry out mutual information maximization learning on the local characteristics and the global characteristics of the nodes:
step 7.1, constructing the d user in the social domain according to the formula (8) and the formula (9)
Figure BDA0003638207970000081
Local feature h of d And the a-th user in the information domain
Figure BDA0003638207970000082
Local feature h of ai ：
h d ＝σ(p d ) (8)
h ai ＝[σ(p a )，σ(q i )] (9)
In the formula (8), sigma is sigmoid nonlinear activation function;
in the formula (9), [, ] is the splicing operation of the vectors;
step 7.2, constructing global features g of all users in the social domain according to the formula (10) and the formula (11) S And global characteristics g of all users in the information domain I ：
Figure BDA0003638207970000083
Figure BDA0003638207970000084
Step 7.3, constructing a discriminator D on the social domain according to the formula (12) and the formula (13) S And a discriminator D on the information field I ：
Figure BDA0003638207970000085
Figure BDA0003638207970000086
In the formula (12), h S Local features expressed as users on the social domain; w S ∈R d×d Is a trainable weight matrix, (h) S ,g S ) Is a positive sample pair on the social domain;
in the formula (13), h I Local features expressed as users on the information domain; w I ∈R 2d×2d Is a trainable weight matrix, (h) I ,g I ) Is a positive sample pair over the information field;
and 7.4, randomly adding and deleting the same number of edges of the heterogeneous graph network G to form a disturbing graph
Figure BDA0003638207970000087
And obtaining local features on the fictitious social domain according to the processes of the step 4, the step 5 and the step 7.1
Figure BDA0003638207970000088
And local features on the imaginary information domain
Figure BDA0003638207970000089
Thus forming negative sample pairs on the social domain
Figure BDA00036382079700000810
And negative example pairs on the information field
Figure BDA00036382079700000811
Step 7.5, constructing a social domain self-supervision loss function L according to the formula (14) and the formula (15) S And information Domain autonomous loss function L I ：
Figure BDA00036382079700000812
Figure BDA00036382079700000813
And 7.5, constructing a total loss function L according to the formula (16):
L＝L R +αL S +λL I (16)
in the formula (16), α and λ are hyper-parameters for balancing two kinds of the auto-supervised loss functions;
7.6, solving all loss functions L by using a gradient descent method to ensure that L converges to a minimum value, thereby obtaining an optimal parameter theta * ＝[P * ,Q * ]Further obtain the optimal user prediction scoring matrix
Figure BDA0003638207970000091
And predicting a scoring matrix according to the optimal user
Figure BDA0003638207970000092
And recommending the product to the non-bridge user in the social domain. The method excavates the inter-domain collaborative information in a very limited bridge user, optimizes the model in a self-supervision learning mode, and fully excavates the global characteristics of the heterogeneous map, thereby realizing more accurate product recommendation of the social cold start user.
Example (b):
in order to verify the effectiveness of the method, the invention performs data processing on two public data sets: epionins, Dianping, to meet the experimental conditions of the present invention.
For the product recommendation task, the invention adopts Hit Ratio (HR) and Normalized counted graphical Gain (NDCG) as evaluation criteria. The invention selects 3 methods for effect comparison, namely EMCDR, NSCR and BiTGCF.
TABLE 1 social Cold Start user product recommendation results on Epines dataset by the method and comparison method of the present invention
Figure BDA0003638207970000093
TABLE 2 social Cold Start user product recommendation results on Dianping datasets by the method of the present invention and the comparison method
Figure BDA0003638207970000094
Specifically, table 1 and table 2 show the experimental results on the epipons and Dianping data sets, respectively, and it can be seen that on the two data sets, the method proposed by the present invention is superior to the 3 methods of comparison in both HR and NDCG indexes on social cold-start users.

Claims (1)

1. A cross-domain social product recommendation method based on self-supervision learning is characterized by comprising the following steps:
step 1, constructing heterogeneous data, comprising: a scoring matrix R of the product by the user and a social relationship matrix S between the users are as follows:
let U I Represents a set of users over an information domain, and
Figure FDA0003638207960000011
wherein the content of the first and second substances,
Figure FDA0003638207960000012
indicating the a-th user in the information field,
Figure FDA0003638207960000013
representing the b-th user in the information domain, M represents the total number of users in the information domain, a is more than or equal to 1, and b is more than or equal to M;
let V denote the product set on the information field, and V ═ V 1 ,…v i ,…v j ,…v L In which v is i Indicating the ith product in the information field, v j Indicating the jth product in the information field, and L indicating the total number of products in the information field;
let R ai Representing the a-th user
Figure FDA0003638207960000014
For the ith product v i The scoring value of (2) is that the scoring matrix of the product by the user on the information domain is R ═ { R ═ R ai } M×L ；
Let U S Represents a set of users on a social domain,
Figure FDA0003638207960000015
wherein the content of the first and second substances,
Figure FDA0003638207960000016
representing the c-th user in the social domain,
Figure FDA0003638207960000017
representing the d-th user in the social domain, N representing the total number of users in the social domain, c being more than or equal to 1, and d being more than or equal to N;
order S cd Represents the c-th user
Figure FDA0003638207960000018
For the d-th user
Figure FDA0003638207960000019
If there is a connection between two users, let S cd If not, let S cd 0, the social relationship matrix between users in the social domain is S ═ S cd } N×N ；
Step 2, utilizing a bridge user set U coexisting in an information domain and a social domain B ＝U I ∩U S Constructing a heterogeneous graph network G ═ G I ∪G S Wherein G is I Representing the set U of all users in the information domain I Graph network structure, G, with product set V S Representing a set U of all users in a social domain S A graph network structure for collecting U-shaped bridge users B The total number of users in (1) is T;
and 3, obtaining a node cooperation matrix through single hot coding, wherein the method comprises the following steps: user cooperation matrix, product cooperation matrix:
step 3.1, constructing a user cooperation matrix P ═ P in a single hot coding mode for all users in the heterogeneous graph network G 1 ,…,p a ,…,p M-T ,…,p b ,…,p c ,…,p M ,…,p d ,…,p M+N-T And P ∈ R (M+N-T)×d Wherein p is a Representing a d-dimensional cooperation vector of an a-th user in the user cooperation matrix P, wherein the a-th user belongs to a non-bridge user in the information domain; p is a radical of b Representing the d-dimensional cooperation vector of the b-th user in the user cooperation matrix P, wherein the b-th user belongs to the bridge user, P d Representing a d-dimensional cooperation vector of a d-th user in the user cooperation matrix P, wherein the d-th user belongs to a non-bridge user in the social domain;
step 3.2, constructing a product cooperation matrix Q ═ Q in a single-hot coding mode for all products in the heteromorphic graph network G 1 ,…,q i ,…q L And Q ∈ R L×d Wherein q is i A d-dimensional collaborative vector representing an ith product;
and 4, carrying out characteristic propagation through the graph volume layer:
step 4.1, making the graph convolution layers have K convolution layers, and initializing the number K of the current convolution layers to be 0;
step 4.2, obtaining the d-dimensional cooperation vector p of the a-th non-bridge user in the information domain according to the formula (1), the formula (2) and the formula (3) respectively a D-dimensional cooperation vector p of the b-th bridge user b D-dimensional collaborative direction of the d-th non-bridge user in social domainQuantity p d After passing through the (k + 1) th graph convolution layer, the corresponding user characteristic vector is obtained
Figure FDA0003638207960000021
Obtaining a d-dimensional cooperative vector q of the ith product according to the formula (4) i The product characteristic vector obtained after passing through the (k + 1) th graph convolution layer
Figure FDA0003638207960000022
Figure FDA0003638207960000023
Figure FDA0003638207960000024
Figure FDA0003638207960000025
Figure FDA0003638207960000026
In the formula (1), R a Representing the set of products that the a-th non-bridge user interacted with,
Figure FDA0003638207960000027
is the d-dimensional cooperation vector p of the a-th non-bridge user in the information domain a When k is 0, let the user characteristic vector output from the k-th convolution layer
Figure FDA0003638207960000028
A user feature vector is initialized randomly;
in the formula (2), R b Represents the product set interacted by the b-th bridge user, S b Representing the b-th bridge userA collection of users having a social connection,
Figure FDA0003638207960000029
is d-dimensional cooperation vector p of the b-th bridge user b When the user characteristic vector output by the k-th convolutional layer is equal to 0,
Figure FDA00036382079600000210
a user feature vector is initialized randomly;
in formula (3), S d Representing a set of users for which the d-th non-bridge user has social connections,
Figure FDA00036382079600000211
the user feature vector is output by the d-th non-bridge user in the social domain at the kth layer convolutional layer, when k is 0,
Figure FDA00036382079600000212
a user feature vector is initialized randomly;
in the formula (4), R i Representing a set of users who have interacted with the ith product,
Figure FDA00036382079600000213
is the d-dimensional cooperation vector q of the ith product i When the characteristic vector of the product output by the k-th layer convolution layer is 0,
Figure FDA00036382079600000214
a randomly initialized product feature vector;
4.3, assigning K +1 to K, judging whether K is more than K, and if so, executing step 5; otherwise, returning to the step 4.2 for sequential execution;
step 5, constructing a node feature aggregation layer:
performing feature aggregation on the K-layer user feature vector and the product feature vector according to the formula (5) to obtain a feature vector p of any user u in the heterogeneous network G u And the feature vector q of the ith product i ：
Figure FDA00036382079600000215
In the formula (5), AVG represents the average pooling operation,
Figure FDA00036382079600000216
a feature vector representing the user u output by the kth convolutional layer,
Figure FDA00036382079600000217
i-th product q representing the output of the k-th convolutional layer i The feature vector of (2);
step 6, constructing a node prediction layer for product recommendation:
step 6.1, calculating the prediction scoring value of the user u on the ith product according to the formula (6)
Figure FDA00036382079600000218
Thereby obtaining a prediction scoring matrix of the product by the user
Figure FDA0003638207960000031
Figure FDA0003638207960000032
In the formula (6), <, > represents a vector inner product operation;
step 6.2, establishing a supervision loss function L according to the formula (7) R ：
Figure FDA0003638207960000033
In formula (7), σ (z) is a nonlinear activation function, and θ ═ P, Q]Is the parameter to be optimized,. psi. I ＝{(u,i,j)|u∈U I ,i∈R u ,j∈(V-R u ) Is aTraining data of all users on the information domain; r u Representing a set of products representing the u-th user interaction;
Figure FDA0003638207960000034
representing the predicted value of credit of the jth product to the jth user;
and 7, constructing an automatic supervision learning layer to carry out mutual information maximization learning on the local characteristics and the global characteristics of the nodes:
step 7.1, constructing the d user in the social domain according to the formula (8) and the formula (9)
Figure FDA0003638207960000035
Local feature h of d And the a-th user in the information domain
Figure FDA0003638207960000036
Local feature h of ai ：
h d ＝σ(p d ) (8)
h ai ＝[σ(p a ),σ(q i )] (9)
In the formula (8), sigma is sigmoid nonlinear activation function;
in the formula (9), [, ] is the splicing operation of the vectors;
step 7.2, constructing global features g of all users in the social domain according to the formula (10) and the formula (11) S And global characteristics g of all users in the information domain I ：
Figure FDA0003638207960000037
Figure FDA0003638207960000038
Step 7.3, constructing a discriminator D on the social domain according to the formula (12) and the formula (13) S And a discriminator D on the information field I ：
Figure FDA0003638207960000039
Figure FDA00036382079600000310
In the formula (12), h S Local features expressed as users on the social domain; w S ∈R d×d Is a trainable weight matrix, (h) S ,g S ) Is a positive sample pair on the social domain;
in the formula (13), h I Local features expressed as users on the information domain; w I ∈R 2d×2d Is a trainable weight matrix, (h) I ,g I ) Is a positive sample pair over the information field;
and 7.4, randomly adding and deleting the same number of edges of the heterogeneous graph network G to form a disturbing graph
Figure FDA00036382079600000311
And obtaining local features on the fictitious social domain according to the processes of the step 4, the step 5 and the step 7.1
Figure FDA00036382079600000312
And local features on the imaginary information domain
Figure FDA0003638207960000041
Thus forming negative sample pairs on the social domain
Figure FDA0003638207960000042
And negative example pairs on the information field
Figure FDA0003638207960000043
Step 7.5, constructing a social domain self-supervision loss function L according to the formula (14) and the formula (15) S And information domain self-monitoringGovernor loss function L I ：
Figure FDA0003638207960000044
Figure FDA0003638207960000045
And 7.5, constructing a total loss function L according to the formula (16):
L＝L R +αL S +λL I (16)
in the formula (16), α and λ are hyper-parameters for balancing two kinds of the auto-supervised loss functions;
7.6, solving all the loss functions L by using a gradient descent method to ensure that L converges to a minimum value, thereby obtaining an optimal parameter theta * ＝[P * ,Q * ]Further obtain the optimal user prediction scoring matrix
Figure FDA0003638207960000046
And predicting a scoring matrix according to the optimal user
Figure FDA0003638207960000047
And recommending the product to the non-bridge user in the social domain.
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