Research On User Community Division Method In E-commerce Heterogeneous Network

In recent years,e-commerce has sprung up and expanded,covering more and more areas,and rapidly leading the trend of social development.E-commerce industry has become a solid force in the development of national economy.Since the end of January 2020,the epidemic of New Coronavirus pneumonia has entered a large-scale outbreak.In this difficult time,the advantages of online operation of e-commerce have gradually emerged.The degree of reemployment affected by the e-commerce industry is lower than that of other industries,and the higher rate of resumption of work is beyond the scope of other industries.E-commerce network has network heterogeneity.Heterogeneous network representation learning and user community division are two main aspects of mining heterogeneous information in e-commerce network.Among them,heterogeneous network representation learning,as a pre order research of user community division,provides accurate user node representation input for the latter.The research on heterogeneous network representation learning and user community Division will help to mine the information resources in heterogeneous e-commerce networks and provide support services for a variety of e-commerce application scenarios,such as customer relationship management,personalized recommendation and so on.Promoting the technological progress of e-commerce platform can improve the quality of life and social development rate of social people,but the existing research results still have room for further improvement.This paper analyzes the existing research results and comes to the conclusion that there are problems in two directions: first,the algorithms for user node representation in e-commerce heterogeneous networks mostly describe the characteristics of user nodes from a single aspect or combine the characteristics of user nodes from several different aspects,and do not make full use of the semantic information of user nodes and network structure information.Second,most of the current user community division algorithms lack consideration of the heterogeneity of e-commerce networks,resulting in low accuracy of community division.For the above problems,this paper proposes a user community division method in e-commerce heterogeneous networks.The method includes the following two parts:1.Aiming at the disadvantages of unreasonable user node representation in e-commerce heterogeneous networks,this paper proposes a user node representation algorithm integrating semantic information and structural information.Firstly,the user's low-order eigenvector is obtained by matrix decomposition SVD optimization;Then,the user high-order feature vector is constructed by using the node transfer characteristics of graph neural network collaborative filtering model;Next,the high-order and low-order feature vectors of users are fused through DCA fusion device,and the edge embedding of user nodes is extracted from e-commerce heterogeneous networks with node attributes;Finally,the inherent attributes of user nodes,the association attributes between users and commodity nodes and the edge embedding of user nodes are embedded as a whole to realize the reasonable representation of user nodes.2.Aiming at the problem that the accuracy of the existing user community partition algorithm is not high,this paper proposes a user community partition algorithm based on multi-layer information fusion in e-commerce heterogeneous networks.Firstly,the heterogeneous e-commerce networks are layered according to different relationship types,and the user node embedding based on different relationship types is constructed;Then,after representation fusion,the users of different layers are embedded and merged to obtain the user fusion embedding representation in e-commerce heterogeneous networks;Next,the objective function is used to optimize the relevant parameters of the user node;Finally,the improved k-means algorithm is used to form user clustering,and the reasonable user community division results are obtained.Based on the data sets of four e-commerce platforms,this paper makes a comparative experiment on the proposed user community division methods in e-commerce heterogeneous networks.The experimental results show that the user node representation algorithm proposed in this paper is feasible,and can represent the characteristics of user nodes more comprehensively and accurately than other mainstream user node representation algorithms.Further experiments show that the user community partition algorithm proposed in this paper has better performance in the accuracy of community partition than the mainstream user community partition algorithm.