Exploring Multi-Dimension User-Item Interactions With Attentional Knowledge Graph Neural Networks for Recommendation

It is commonly agreed that a recommender system should use not only explicit information (i.e., historical user-item interactions) but also implicit information (i.e., incidental information) to deal with the problem of data sparsity and cold start. The knowledge graph (KG), due to its expressive structural and semantic representation capabilities, has been increasingly used for capturing auxiliary information for recommender systems, such as the recent development of graph neural network (GNN) based models for KG-aware recommendation. Nevertheless, these models have the shortcoming of insufficient node interactions or improper node weights during information propagation, which limits the performance of recommender systems. To address this issue, we propose a Multi-dimension Interaction based attentional Knowledge Graph Neural Network (MI-KGNN) for enhanced KG-aware recommendation. MI-KGNN characterizes similarities between users and items through information propagation and aggregation in knowledge graphs. As such, it can optimize the updating direction of node representation by fully exploring multi-dimension interactions among nodes during information propagation. In addition, MI-KGNN introduces a dual attention mechanism, which allows users and items to jointly determine the weight of neighbor nodes. As a result, MI-KGNN can effectively capture and represent both structural (i.e., the topology of interactions) and semantic information (i.e., the weight of interactions) in the knowledge graph. Experimental results show that the proposed model significantly outperforms baseline methods for top-K recommendation. Specifically, the recall rate is increased by 5.78%, 6.66%, and 3.22% on three public datasets, compared with the best performance of existing methods.