Research On Dynamic Heterogeneous Network Representation Embedding For Recommendation

The traditional recommendation methods mainly use matrix factorization technology to extract the effective features of users.This type of methods requires lots of explicit feedback from users,and is difficult to scale to complex and large-scale data.Besides,cold start problems may occur due to sparse data.Since the interaction data between users and items can essentially be abstracted as a graph(or network)in a non-Euclidean space,recommendation methods based on graph learning have received great attention.Network representation learning aims to obtain the embedding of nodes which retains structural information and semantic information,and can be applied to recommendation tasks.This type of methods currently still has the following challenges:(1)Lots of studies build networks based on explicit feedback of users.However,this kind of data is hard to obtain,resulting in data sparsity problem;(2)Many methods ignore the evolution of user preferences over time.The interactive data between users and items is built as a static network.To handle the above challenges,this article systematically performed the following work.(1)This paper uses a large number of multiple types of implicit feedback between users and items to construct a multi-behavior network,and proposes the Ri Do TA model.First,the multi-behavior network is separated into a base network,multiple source networks and a target network;then,this model jointly optimizes the embedding representation of nodes in each network based on random walk strategy;then,so as to learn the impact of different implicit feedback behaviors on target behavior(explicit feedback),attention mechanism is applied to adaptively obtain the importance of each source network;finally,the learned embedding of users and items is concatenated as the input of multi-layer perceptron to learn the complex nonlinear relationship and predict the preference score between users and items.(2)To model user preferences that evolve over time,this paper integrates dynamic network methods into recommendation tasks and proposes the DBNRec model.First,DBNRec uses user-item interaction data to construct a dynamic bipartite network and defines temporal edge weights;second,graph convolutional networks are used to aggregate the first-order and high-order neighbor information of two types of nodes in the network.Embedding of nodes is updated through information construction and information dissemination.Considering the heterogeneity of the nodes in the bipartite network,a conversion matrix is introduced when the two types of node embedding matrices are multiplied;finally,the embeddings of users and items are spliced into the multi-layer perceptron for predictions.