on Mining User-Item Interactions via Knowledge Graph for Recommendation

Abstract

Introducing Knowledge Graph (KG) to facilitate recommender system has become a tendency in recent years. Many existing methods leverage KG to obtain side information of items to promote item representation learning for enhancing recommendation performance. However, they ignore that KG also may contribute to better user representation learning. To solve the above issue, we propose a novel algorithm, KIGR ( K nowledge-aware I nteraction G raph for R ecommendation), to mine user-item interactions via Knowledge Graph for assisting user representation learning. Specifically, a user-item interaction is encoded by attentively summing up the relation embedding about the item in KG. Then an unsupervised learning method is used to group the user-item interactions into different latent types. Further, a user-item interaction graph is divided into several subgraphs, which is referred to as Knowledge-aware Interaction Graph, making each subgraph only contains one latent type of interactions. Finally, user representation is the fusion of user interest embedding, which is learned on knowledge-aware interaction graph; While item representation is learned on KG. Experimental results on MovieLens, LastFM and Amazon-Book validate that the proposed KIGR has a superior performance compared with the SOTA algorithms.