Research on Neural Graph Collaborative Filtering Recommendation Model Fused With Item Temporal Sequence Relationships

Abstract

Graph neural network-based recommender systems are blossoming recently, and it can explicitly express user-item high-order connectivity information, so it can significantly improve the recommendation performance. However, the existing methods usually assume that the users' interests are invariant, but temporal relationships is left insufficient exploration to charactertize the user's dynamic interest. In this paper, we propose a Neural Graph Collaborative Filtering recommendation model fused with Item Temporal Sequence relationships (NGCF-ITS), that is, a hybrid recommendation model that fuses with user-item interactions information and item temporal sequence relationships. It divides the item temporal sequences into several groups of subsequences through the sliding window strategy, then constructs the item temporal sequence relationships graph and aggregates the characteristics of item temporal sequences information. At the last, deeply depicts the dynamic changes of users' interests, and uses the bipartite graph neural network to map the high-dimensional information of user-item and item-item into the low-dimensional space. The hybrid embedding of user-item historical interactions and item temporal sequence relationships are realized, and the expression of user-item interactions is enhanced. In this way, the heterogeneous multi-relational graphs are fused for the feature propagation, which largely refines the user and item representation for model prediction. Extensive experiments demonstrate the our proposed model significantly improve the recommendation performance compared to the state-of-the-art GNN-based models both in accuracy and training efficiency.