SARC: Split-and-Recombine Networks for Knowledge-Based Recommendation

Abstract

Utilizing knowledge graphs (KGs) to improve the performance of recommender systems has attracted increasing attention recently. Existing path-based methods rely heavily on manually designed meta-paths, while embedding-based methods focus on incorporating the knowledge graph embeddings (KGE) into recommender systems, but rarely model user-entity interactions, which can be used to enhance the performance of recommendation. To overcome the shortcomings of previous works, we propose SARC, an embedding-based model that utilizes a novel Split-And-ReCombine strategy for knowledge-based recommendation. Firstly, SARC splits the user-item-entity interactions into three 2-way interactions, i.e., the user-item, user-entity and item-entity interactions. Each of the 2-way interactions can be cast as a graph, and we use Graph Neural Networks (GNN) and KGE to model them. Secondly, SARC recombines the representation of users and items learned from the first step to generates recommendation. In order to distinguish the informative part and meaningless part of the representations, we utilize a gated fusion mechanism. The advantage of our SARC model is that through splitting, we can easily handle and make full use of the 2-way interactions, especially the user-entity interactions, and through recombining, we can extract the most useful information for recommendation. Extensive experiments on three real-world datasets demonstrate that SARC outperforms several state-of-the-art baselines.