Reducing noise-triplets via differentiable sampling for knowledge-enhanced recommendation with collaborative signal guidance

Abstract

Knowledge Graph (KG) is widely used for recommendation tasks due to its rich semantic information and external structure. Current knowledge graph recommendation models are insufficient for the learning of user–item interactive behaviors as well as external knowledge, and ignore the noise information in KG. To alleviate these limitations, we present a Collaborative Relation-aware Attention Network with Differentiable sampling (DCRAN) that can fully learn user–item interactions and knowledge graphs, and accurately select recommendation-related entities in KG. Specifically, DCRAN explicitly learns the embeddings of users and items and encodes them into collaborative interaction signals, which are used as guidance signals to extract knowledge. Furthermore, DCRAN emphasizes the importance of KG relations and constructs a relation-aware attention network to learn the representation of items in KG. Most importantly, DCRAN applies a differentiable sampling strategy on entities, which can reduce triplets that have a negative effect on recommendation. Experimental results on three public datasets manifest the effectiveness of DCRAN.