A better understanding of the interaction between users and items by knowledge graph learning for temporal recommendation

Abstract

Recently the knowledge graph (KG) as extra auxiliary information is widely used to improve recommendation. Existing methods usually treat knowledge representation as characteristic information for addressing data sparsity and cold start issues. However, they ignore the implicit and explicit interaction between users and items, which may be gained by the relation extraction and knowledge reasoning, to lead to suboptimal performance. Thus, we believe that it is crucial to incorporate both relations and attributes of users and items into recommender system. That can better capture the extent that a user prefer to an item. In this paper, we propose a novel knowledge graph-based temporal recommendation (KGTR) model. Firstly, we design a lightweight KG on the basis of a single independent domains knowledge without extra supplement. We define three relationships to express interactions within/between users and items, including the interaction of a user browsing an item, the social relation of two users browsing one item, and the behavior of a user browsing items in the meantime. Different from previous knowledge translation-based recommendation methods, we embed interactions by adding them to the transformation from one entity to another in KG. Extensive experiments on real world dataset show that our KGTR outperforms several state-of-the-art recommendation methods.