CN111737591A - Product recommendation method based on heterogeneous heavy-side information network translation model - Google Patents
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Abstract
The invention relates to the technical field of heterogeneous information networks, and discloses a product recommendation method based on a heterogeneous heavy-edge information network translation model. Firstly, performing fusion expression space learning on the multi-element heavy edge relation between node pairs in the heterogeneous heavy edge information network by adopting an attention mechanism; the type information of the heterogeneous nodes is projected to a multivariate heavy-edge relation fusion expression space, and the distributed expression of the heterogeneous nodes and the relation is realized on the multivariate heavy-edge relation fusion expression space by using a translation model; and then carrying out node prediction research based on the representation of the heterogeneous nodes and the fused representation of the multi-element multiple edge relation. The heterogeneous node embedding based on the multivariate repeated edge relation can improve the accuracy of heterogeneous repeated edge information network link prediction, has higher performance than the traditional representation learning method, and has higher practical value in the aspect of commodity recommendation based on the multivariate relation.
Description
Technical Field
The invention relates to the technical field of heterogeneous heavy-edge information networks, in particular to a product recommendation method based on a heterogeneous heavy-edge information network translation model.
Background
The development of social media enables users to publish content of interest to the users anytime and anywhere. The social activity of the user is improved by the release of the user interest content, the social relationship of the user is enhanced, and a complex social network is formed. The rapid development of electronic commerce enables the interaction between users and products to be more and more frequent, and the interaction between a huge user group and a plurality of products also forms a complex network. The social network and the E-commerce network are provided with a plurality of types of interactive objects, and the interactive objects have different types of relations, so that a heterogeneous information network is formed. The method for predicting the relationship between objects based on the heterogeneous information network has become a hot research direction of the link prediction and recommendation system of the current social network and electronic commerce platform. In a complex heterogeneous information network, the relationship between any node object may have various kinds, and diversified relationships may be formed between the objects. That is, in a heterogeneous information network, each node object may have multiple types, and any two node objects may have multiple relationship edges, forming a special network, i.e., a heterogeneous heavy-edge information network.
The translation models in the knowledge graph, such as TransE, TransD, TransH, TransR and the like, effectively acquire semantic information of objects and relations by learning distributed representation of head nodes and tail nodes in triple pairs through a translation mechanism, and obtain remarkable performance in the aspect of link prediction. The object types of the node pairs in the heterogeneous heavy-edge information network are different, multiple heavy-edge relations exist among the nodes, the method that the traditional translation model mainly processes the single type relations among the node objects is not applicable any more, and the semantic representation capability of entities in the knowledge graph can be improved by fusing the multiple heavy-edge relations among the node pairs in the heterogeneous heavy-edge information network. Semantic information of entity objects of the heterogeneous nodes can be learned on the basis of a translation model in a multivariate heavy-edge relation fusion expression space of the heterogeneous nodes, and link sequencing recommendation among the nodes is achieved.
Disclosure of Invention
Aiming at the problems, the invention provides a product recommendation method based on a heterogeneous heavy-side information network translation model.
In order to achieve the purpose, the invention adopts the following technical scheme:
a product recommendation method based on a heterogeneous heavy-side information network translation model comprises the following steps:
step S1, performing fusion representation space learning on the multi-element heavy edge relation between the node pairs in the heterogeneous heavy edge information network by adopting an attention mechanism;
step S2, projecting the type information of the heterogeneous nodes to a multivariate heavy-edge relation fusion expression space;
step S3, an embedded model of heterogeneous heavy edges is provided by using a translation mechanism on the multivariate heavy edge relation fusion expression space, and distributed expression of heterogeneous nodes and relations is realized;
in step S4, node prediction is performed based on the fused representation space of the representation of the node and the multiple edge relation.
Further, in the step S1, performing fusion representation space learning on the multivariate heavy-edge relationship between the node pairs in the heterogeneous heavy-edge information network by using an attention mechanism further includes the following steps:
step S1.1, setting the heterogeneous information network G ═ V, E, where V is a set of nodes and E is a set of edges; in heterogeneous information networks, there is a mapping ψ V → T of a set of nodes V to a set of node types T and a mapping E of a set of edges E to a set of edge types AAnd | T | + | A | > 2; different types of nodes in the heterogeneous information network reflect different semantic information of the nodes, and can be used for describing different semantic features of users or products in the social network; different types of edges in the heterogeneous information network reflect different interactive semantic information, and can be used for modeling different behaviors between users and products and capturing different behavior semantics between the users and the products;
step S1.2, setting a heterogeneous heavy-edge information network G ' ═ V ', E ', where V ' is a set of nodes and E ' is a set of edges; there is a mapping ψ 'of the node set V' to the node type set T V '→ T and a mapping of the edge set E' to the edge type set AAnd | T | + | AIf the binary group (u, v) ∈ E 'exists at the same time, and the number of edges of the associated node pair (u, v) is more than 1, G' is called as a heterogeneous heavy-edge information network, a plurality of connecting edges of the node pair (u, v) in the heterogeneous heavy-edge information network reflect various interactive behavior relations among the nodes (u, v), and rich social behaviors between learning users and products can be modeled so as to provide accurate product recommendation for the users;
step S1.3, in the heterogeneous heavy-edge information network G '(V', E '), given a bigram (u, V) ∈ E', given a set of multiple heavy-edge relationships between node pairs as R ═ R | R ∈ a }, R representing an arbitrary type of relationship between node pairs (u, V), and R representing a set of different types of relationships between node pairs (u, V), then a set of trigram pairs of multiple heavy-edge relationships are denoted as (u, R, V), and given a behavior relationship R between node pairs (u, V)i∈ R, the mapping process is defined as:
wherein, aiRepresents a certain behavioral relationship r of node pairs (u, v)iVector representation ofThe linear transformation of (a) to (b),the shared parameter is used for learning different importance of the multiple heavy edge relation; a isiBy using the theory of spatial transformation, the invention focuses on the relation riVector representation ofThe projection is mapped onTransforming the space to obtain new features;
s1.4, calculating the mapping characteristics of any relation for all behavior relations R of the node pairs (u, v), and pairing the relation R through a softmax functioniThe normalization weight coefficients are:
wherein, αiRepresents the relation riWeight coefficient in all behavioral relationships of node pair (u, v), e is a natural constant, akIs the relation r of node pair (u, v)k(k ═ 1, 2., | | R | |), the number of elements in the multiple heavy-edge relation set R |, and a weight coefficient αiDifferent importance of the node pair (u, v) behavior relation is described, and recommendation of products based on the behavior relation with large weight is facilitated;
s1.5, reflecting different importance of different types of relations in the node pair (u, v) connection by weight coefficients; then, the multivariate and heavy-edge relationship fusion expression space of the node pairs is learned through the attention weight coefficient, and is recorded as:
wherein the content of the first and second substances,vector weighted fusion representing the multivariate multiple edge relationship of node pairs (u, v),is a relation riA vector representation of (a); fused representation spaceLinear combinations of different types of relationships of node pairs (u, v) are depicted, which can explain the synergy of various interaction behaviors of social network users and products.
Further, the step S2, projecting the type information of the heterogeneous node to the multivariate heavy-edge relationship fusion expression space further includes the following steps:
s2.1, in the heterogeneous heavy-edge information network, giving a triple heterogeneous node pair (u, R, v) of a multi-element heavy-edge relation, wherein u and v have heterogeneous types; the heterogeneous nodes u and v have a multiple heavy edge relation, and the multiple heavy edge relation is set as R; r describes various interactive behaviors between a user and a product;
step S2.2Respectively mapping a head node u and a tail node v of the node pair (u, v) to a uniform potential semantic space; the representation of the head node u and the tail node v in the unified space can be expressed as:
wherein the content of the first and second substances,different types of spaces T for head node u and tail node vu、TvThe learning parameter matrix of (2);vector representations of a head node u and a tail node v, respectively; vector representation of heterogeneous type head node u and tail node vFrom the space T of the typeu、TvRespectively mapping to a uniform space phi to obtain a vector representation of the uniform space phiThe vector representation of the uniform space is beneficial to realizing the latent space semantic projection of the user and product type information, and the representation between the user and the product can be learned on a uniform scale;
step S2.3, considering the relevance of the head node and the tail node on the multi-element heavy edge relation fusion semantic space, adopting the concept of TransR to map the potential space representation of the head node and the tail node to the multi-element heavy edge relation fusion semantic space, and establishing the representation of the head node and the tail node, namely:
wherein the content of the first and second substances,a projection parameter matrix of the expression space is fused for the multivariate heavy-edge relation; representing vectors of a head node u and a tail node v in phi spaceVector mapping to multiple heavy edge relation fusion expression spaceTo obtain a fused representation spaceRepresentation of vectors inThe distributed representation between the users and the products is learned on the multivariate heavy-edge relation fusion representation space, and the establishment of the relation between the users and the products on the multivariate type interactive behavior relation is facilitated.
Further, in the step S3, the method for providing an embedded model of heterogeneous heavy edges by using a translation mechanism in the multivariate heavy edge relationship fusion expression space, and implementing distributed expression of heterogeneous nodes and relationships further includes the following steps:
s3.1, adopting the idea of knowledge representation translation, and establishing the association between the heterogeneous node pairs (u, v) through a translation mechanism on the multivariate and multiple edge relation fusion representation space, wherein the association is formed as follows:
based on the idea of translation, word2vec translation invariance is used, distributed representation of head nodes and tail nodes of heterogeneous types in a low-dimensional dense space can be achieved, the semantics of the head nodes and the tail nodes of the heterogeneous types are represented, and rich semantic information of users and products can be described;
step S3.2, for a triple heterogeneous node pair (u, R, v) with a multiple heavy-edge relationship, a distance function based on the translation mechanism may be defined as:
wherein f isR(u, v) represents the fusion of the head node u and the tail node v in the multi-element heavy-edge relation representation spaceA distance function of (a);representing the vector representation of the head node u on the multivariate heavy edge relation fusion representation space;representing vector representation of the tail node v on the multivariate heavy edge relation fusion representation space;a fusion representation space of a multivariate heavy edge relation set R representing a head node u and a tail node v; the preference distance of the user to the product can be in the fusion representation space of the multiple edge relation set RAnd calculating, and based on the multivariate heavy edge relation, realizing accurate sequencing recommendation of the product by the user.
Step S3.3 for the positive sample triplet (u, R, v) and the negative sample triplet (u, R, v) according to the distance function of the triplet heterogeneous node pair (u, R, v)Triple unitDefining a translation-relationship-based Hinge Loss function as:
wherein S is a set of (u, R, v) positive samples, and a head node u and a tail node v have a multiple-element multiple-edge relation R;is a set of negative examples of the negative samples,is a head node based on (u, R, v) replacement, andthere is no connection edge relation or multiple edge relation R with v,is a tail node based on (u, R, v) replacement, and u isNo continuous edge relation or multiple edge relation R exists, gamma represents boundary parameters, lambda represents a hyper-parameter, η represents a regularization hyper-parameter;representing the vector representation of the head node u on the multivariate heavy edge relation fusion representation space;representing vector representation of the tail node v on the multivariate heavy edge relation fusion representation space;representing head nodeVector representation on the multi-element heavy-edge relation fusion representation space;representing tail nodesVector representation on the multi-element heavy-edge relation fusion representation space; t isu、TvType spaces of a head node u and a tail node v respectively;andorthogonality ensures that different types of heterogeneous head nodes and tail nodes can learn different parameter matrixes; the method comprises the steps that Hinge Loss learns the distance between a head node and a tail node in a space through multivariate heavy edge relation fusion, Hinge Loss minimization requires that the distance of a positive sample is smaller than that of a negative sample and is lower than a boundary parameter gamma, and distributed representation of the head node and the tail node is iteratively learned; the smaller distance between the positive samples is learned to the user, and semantic representation of the product is beneficial to selecting favorite products for the user on the multivariate and repeated edge relation fusion representation space.
Further, in step S4, the method for node prediction based on the fused representation space of the node representation and the multi-element multiple edge relationship is to adopt the new node triple (u, R, vDetermining the distance between the node v' and the head node u, and sorting in the candidate set according to the distance; fused representation space in a multi-element heavy edge relation RAnd calculating a distance function between the projection of the user and the projection of the product, wherein the product with smaller distance from the user is semantically similar to the user, and is the product preferred by the user.
The invention provides a product recommendation method based on a heterogeneous heavy-side information network translation model. Firstly, performing fusion expression space learning on multi-element heavy edge relations between node pairs in a heterogeneous heavy edge information network by adopting an attention mechanism, then projecting and learning type information of heterogeneous nodes to a multi-element heavy edge relation fusion expression space, providing an embedded model of the heterogeneous heavy edges on the multi-element heavy edge relation fusion expression space by utilizing a translation mechanism, realizing distributed expression of the heterogeneous nodes and relations, and then performing node prediction research on the basis of the expression of the nodes and the fusion expression space of the multi-element heavy edge relations. The recommended ordering of the nodes is the final output result of the invention.
Compared with the prior art, the invention has the following advantages:
the product recommendation method based on the heterogeneous heavy-edge information network translation model is different from the existing model and is characterized in that fusion representation space learning is carried out on multi-element heavy-edge relations among node pairs in the heterogeneous heavy-edge information network by adopting an attention mechanism, the type information of heterogeneous nodes is projected and learned to the multi-element heavy-edge relation fusion representation space, an embedded model of heterogeneous heavy edges is provided on the multi-element heavy-edge relation fusion representation space by utilizing a translation mechanism, distributed representation of the heterogeneous nodes and relations is realized, the accuracy of heterogeneous heavy-edge information network link prediction can be improved, the performance is higher than that of the traditional representation learning method, and the product recommendation method has higher practical value in the aspect of commodity recommendation.
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FIG. 1 is a schematic diagram of an overall model architecture.
Detailed Description
The method for identifying and marking the attention relationship based on the user hierarchical theme preference semantic matrix is implemented by a computer program. The following is a detailed description of the embodiments of the present invention according to the flow chart. FIG. 1 is a schematic diagram of an overall model architecture.
A product recommendation method based on a heterogeneous heavy-side information network translation model comprises the following steps:
step S1, performing fusion representation space learning on the multi-element heavy edge relation between the node pairs in the heterogeneous heavy edge information network by adopting an attention mechanism;
step S1.1, setting the heterogeneous information network G ═ V, E, where V is a set of nodes and E is a set of edges; in heterogeneous information networks, there is a mapping ψ V → T of a set of nodes V to a set of node types T and a mapping E of a set of edges E to a set of edge types AAnd | T | + | A | > 2;
step S1.2, setting a heterogeneous heavy-edge information network G ' ═ V ', E ', where V ' is a set of nodes and E ' is a set of edges; there is a mapping ψ 'of the node set V' to the node type set T V '→ T and a mapping of the edge set E' to the edge type set AIf two tuples (u, v) ∈ E 'exist at the same time and the number of edges of the associated node pair (u, v) is more than 1, G' is called a heterogeneous heavy-edge information network;
step S1.3, in the heterogeneous heavy-edge information network G '(V', E '), given a bigram (u, V) ∈ E', given a set of multiple heavy-edge relationships between node pairs as R ═ R | R ∈ a }, R representing an arbitrary type of relationship between node pairs (u, V), and R representing a set of different types of relationships between node pairs (u, V), then a set of trigram pairs of multiple heavy-edge relationships are denoted as (u, R, V), and given a behavior relationship R between node pairs (u, V)i∈ R, the mapping process is defined as:
wherein, aiRepresents a certain behavioral relationship r of node pairs (u, v)iVector representation ofThe linear transformation of (a) to (b),for sharing parameters, for learning moreDifferent importance of the meta-heavy edge relationship;
s1.4, calculating the mapping characteristics of any relation for all behavior relations R of the node pairs (u, v), and pairing the relation R through a softmax functioniThe normalization weight coefficients are:
wherein, αiRepresents the relation riWeight coefficient in all behavioral relationships of node pair (u, v), e is a natural constant, akIs the relation r of node pair (u, v)k(k ═ 1,2, ·, | | R | |), where | R | | is the number of elements in the set of multivariate multiple edge relationships R;
step S1.5 the weighting factors reflect the different importance of different types of relationships in the node pair (u, v) connection. Then, the multivariate and heavy-edge relationship fusion expression space of the node pairs is learned through the attention weight coefficient, and is recorded as:
wherein the content of the first and second substances,vector weighted fusion representing the multivariate multiple edge relationship of node pairs (u, v),is a relation riIs represented by a vector of (a).
Step S2, projecting the type information of the heterogeneous nodes to a multivariate heavy-edge relation fusion expression space;
s2.1, in the heterogeneous heavy-edge information network, giving a triple heterogeneous node pair (u, R, v) of a multi-element heavy-edge relation, wherein u and v have heterogeneous types; the heterogeneous nodes u and v have a multiple heavy edge relation, and the multiple heavy edge relation is set as R;
step S2.2Respectively mapping a head node u and a tail node v of the node pair (u, v) to a uniform potential semantic space; the representation of the head node u and the tail node v in the unified space can be expressed as:
wherein the content of the first and second substances,different types of spaces T for head node u and tail node vu、TvThe learning parameter matrix of (2);vector representations of a head node u and a tail node v, respectively; vector representation of heterogeneous type head node u and tail node vFrom the space T of the typeu、TvRespectively mapping to a uniform space phi to obtain a vector representation of the uniform space phi
Step S2.3, considering the relevance of the head node and the tail node on the multi-element heavy edge relation fusion semantic space, adopting the concept of TransR to map the potential space representation of the head node and the tail node to the multi-element heavy edge relation fusion semantic space, and establishing the representation of the head node and the tail node, namely:
wherein the content of the first and second substances,a projection parameter matrix of the expression space is fused for the multivariate heavy-edge relation; representing vectors of a head node u and a tail node v in phi spaceVector mapping to multiple heavy edge relation fusion expression spaceTo obtain a fused representation spaceRepresentation of vectors in
Step S3, an embedded model of heterogeneous heavy edges is provided by using a translation mechanism on the multivariate heavy edge relation fusion expression space, and distributed expression of heterogeneous nodes and relations is realized;
s3.1, adopting the idea of knowledge representation translation, and establishing the association between the heterogeneous node pairs (u, v) through a translation mechanism on the multivariate and multiple edge relation fusion representation space, wherein the association is formed as follows:
step S3.2, for a triple heterogeneous node pair (u, R, v) with a multiple heavy-edge relationship, a distance function based on the translation mechanism may be defined as:
wherein f isR(u, v) represents the fusion of the head node u and the tail node v in the multi-element heavy-edge relation representation spaceA distance function of (a);representing the vector representation of the head node u on the multivariate heavy edge relation fusion representation space;representing vector representation of the tail node v on the multivariate heavy edge relation fusion representation space;a fusion representation space of a multivariate heavy edge relation set R representing a head node u and a tail node v;
step S3.3 for the positive sample triplet (u, R, v) and the negative sample triplet (u, R, v) according to the distance function of the triplet heterogeneous node pair (u, R, v)Defining a translation-relationship-based Hinge Loss function as:
wherein S is a set of (u, R, v) positive samples, and a head node u and a tail node v have a multiple-element multiple-edge relation R;is a set of negative examples of the negative samples,is a head node based on (u, R, v) replacement, andthere is no connection edge relation or multiple edge relation R with v,is a tail node based on (u, R, v) replacement, and u isNo continuous edge relation or multiple edge relation R exists, gamma represents boundary parameters, lambda represents a hyper-parameter, η represents a regularization hyper-parameter;representing the vector representation of the head node u on the multivariate heavy edge relation fusion representation space;representing vector representation of the tail node v on the multivariate heavy edge relation fusion representation space;representing head nodeVector representation on the multi-element heavy-edge relation fusion representation space;representing tail nodesVector representation on the multi-element heavy-edge relation fusion representation space; t isu、TvType spaces of a head node u and a tail node v respectively;andthe orthogonality ensures that different types of heterogeneous head nodes and tail nodes can learn different parameter matrixes.
In step S4, node prediction is performed based on the representation of the node and the fused representation of the multi-element multiple edge relationship.
In particular for new node triplets (u, R, v'), adoptAnd determining the distance between the node v' and the head node u, and sorting in the candidate set according to the distance.
Evaluation of Effect
In order to verify the effectiveness and the advancement of the technical scheme provided by the invention, several existing translation model methods are selected for comparison: TransE and TransR. In addition, we generated new TransE + Multi-edges and TransR + Multi-edges models for comparison by replacing the relationship of the TransE and TransR models with the multivariate heavy-edge relationship fusion representation of the attention mechanism of the present invention. The link prediction results of the method on the partial data set scored by the MovieLens100K movie are evaluated through Hits @ K, and the results are shown in table 1:
TABLE 1
The results in the table show that the technical scheme of the invention can obtain the results with better precision and reliability than the existing method when the user carries out the link prediction of the movie recommendation.
Those skilled in the art will appreciate that the invention may be practiced without these specific details. Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.
Claims (5)
1. A product recommendation method based on a heterogeneous heavy-side information network translation model is characterized by comprising the following steps: the method comprises the following steps:
step S1, performing fusion representation space learning on the multi-element heavy edge relation between the node pairs in the heterogeneous heavy edge information network by adopting an attention mechanism;
step S2, projecting the type information of the heterogeneous nodes to a multivariate heavy-edge relation fusion expression space;
step S3, an embedded model of heterogeneous heavy edges is provided by using a translation mechanism on the multivariate heavy edge relation fusion expression space, and distributed expression of heterogeneous nodes and relations is realized;
in step S4, node prediction is performed based on the fused representation space of the representation of the node and the multiple edge relation.
2. The product recommendation method based on the heterogeneous heavy-side information network translation model according to claim 1, wherein: in the step S1, performing fusion representation space learning on the multivariate heavy edge relationship between the node pairs in the heterogeneous heavy edge information network by using an attention mechanism further includes the following steps:
step S1.1, setting the heterogeneous information network G ═ V, E, where V is a set of nodes and E is a set of edges; in heterogeneous information networks, there is a mapping ψ V → T of a set of nodes V to a set of node types T and a mapping E of a set of edges E to a set of edge types AAnd | T | + | A | > 2;
step S1.2, setting a heterogeneous heavy-edge information network G ' ═ V ', E ', where V ' is a set of nodes and E ' is a set of edges; there is a mapping ψ 'of the node set V' to the node type set T V '→ T and a mapping of the edge set E' to the edge type set AIf two tuples (u, v) ∈ E 'exist at the same time and the number of edges of the associated node pair (u, v) is more than 1, G' is called a heterogeneous heavy-edge information network;
step S1.3, in the heterogeneous heavy-edge information network G '(V', E '), given a bigram (u, V) ∈ E', given a set of multiple heavy-edge relationships between node pairs as R ═ R | R ∈ a }, R representing an arbitrary type of relationship between node pairs (u, V), and R representing a set of different types of relationships between node pairs (u, V), then a set of trigram pairs of multiple heavy-edge relationships is denoted as (u, R, V), and for node pairs (u, V), a set of trigram pairs of multiple heavy-edge relationships is denoted as (u, R, V)Behavioral relationship ri∈ R, the mapping process is defined as:
wherein, aiRepresents a certain behavioral relationship r of node pairs (u, v)iVector representation ofThe linear transformation of (a) to (b),the shared parameter is used for learning different importance of the multiple heavy edge relation;
s1.4, calculating the mapping characteristics of any relation for all behavior relations R of the node pairs (u, v), and pairing the relation R through a softmax functioniThe normalization weight coefficients are:
wherein, αiRepresents the relation riWeight coefficient in all behavioral relationships of node pair (u, v), e is a natural constant, akIs the relation r of node pair (u, v)k(k ═ 1,2, ·, | | R | |), where | R | | is the number of elements in the set of multivariate multiple edge relationships R;
s1.5, reflecting different importance of different types of relations in the node pair (u, v) connection by weight coefficients; learning a multivariate heavy-edge relation fusion expression space of the node pairs through the attention weight coefficients, and recording the multivariate heavy-edge relation fusion expression space as:
3. The product recommendation method based on the heterogeneous heavy-side information network translation model according to claim 1, wherein: in the step S2, projecting the type information of the heterogeneous node to the multivariate multiple-edge relationship fusion representation space further includes the following steps:
s2.1, in the heterogeneous heavy-edge information network, giving a triple heterogeneous node pair (u, R, v) of a multi-element heavy-edge relation, wherein u and v have heterogeneous types; the heterogeneous nodes u and v have a multiple heavy edge relation, and the multiple heavy edge relation is set as R;
step S2.2Respectively mapping a head node u and a tail node v of the node pair (u, v) to a uniform potential semantic space; the representation of the head node u and the tail node v in the unified space can be expressed as:
wherein the content of the first and second substances,different types of spaces T for head node u and tail node vu、TvThe learning parameter matrix of (2);vector representations of a head node u and a tail node v, respectively; vector representation of heterogeneous type head node u and tail node vFrom the space T of the typeu、TvRespectively mapping to a uniform space phi to obtain a vector representation of the uniform space phi
Step S2.3, considering the relevance of the head node and the tail node on the multi-element heavy edge relation fusion semantic space, adopting the concept of TransR to map the potential space representation of the head node and the tail node to the multi-element heavy edge relation fusion semantic space, and establishing the representation of the head node and the tail node, namely:
wherein the content of the first and second substances,a projection parameter matrix of the expression space is fused for the multivariate heavy-edge relation; representing vectors of a head node u and a tail node v in phi spaceVector mapping to multiple heavy edge relation fusion expression spaceTo obtain a fused representation spaceRepresentation of vectors in
4. The product recommendation method based on the heterogeneous heavy-side information network translation model according to claim 1, characterized in that: in the step S3, the method for extracting an embedded model of heterogeneous heavy edges by using a translation mechanism in the multivariate heavy edge relationship fusion expression space to realize the distributed expression of heterogeneous nodes and relationships further includes the following steps:
s3.1, adopting the idea of knowledge representation translation, and establishing the association between the heterogeneous node pairs (u, v) through a translation mechanism on the multivariate and multiple edge relation fusion representation space, wherein the association is formed as follows:
step S3.2, for a triple heterogeneous node pair (u, R, v) with a multiple heavy-edge relationship, a distance function based on the translation mechanism may be defined as:
wherein f isR(u, v) represents the fusion of the head node u and the tail node v in the multi-element heavy-edge relation representation spaceA distance function of (a);representing the vector representation of the head node u on the multivariate heavy edge relation fusion representation space;representing vector representation of the tail node v on the multivariate heavy edge relation fusion representation space;a fusion representation space of a multivariate heavy edge relation set R representing a head node u and a tail node v;
step (ii) ofS3.3 for positive sample triplets (u, R, v) and negative sample triplets according to the distance function of the triple heterogeneous node pair (u, R, v)Defining a translation-relationship-based Hinge Loss function as:
wherein S is a set of (u, R, v) positive samples, and a head node u and a tail node v have a multiple-element multiple-edge relation R;is a set of negative examples of the negative samples,is a head node based on (u, R, v) replacement, andthere is no connection edge relation or multiple edge relation R with v,is a tail node based on (u, R, v) replacement, and u isNo continuous edge relation or multiple edge relation R exists, gamma represents boundary parameters, lambda represents a hyper-parameter, η represents a regularization hyper-parameter;representing the vector representation of the head node u on the multivariate heavy edge relation fusion representation space;representing vector representation of the tail node v on the multivariate heavy edge relation fusion representation space;representing head nodeVector representation on the multi-element heavy-edge relation fusion representation space;representing tail nodesVector representation on the multi-element heavy-edge relation fusion representation space; t isu、TvType spaces of a head node u and a tail node v respectively;andthe orthogonality ensures that different types of heterogeneous head nodes and tail nodes can learn different parameter matrixes.
5. The product recommendation method based on the heterogeneous heavy-side information network translation model according to claim 1, characterized in that: in step S4, the method for node prediction based on the fused representation space of the node representation and the multiple heavy edge relation is to adopt the new node triple (u, R, vAnd determining the distance between the node v' and the head node u, and sorting in the candidate set according to the distance.
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