Entity-aware Collaborative Relation Network with Knowledge Graph for Recommendation

Abstract

As the source of side information, knowledge graph (KG) plays a critical role in recommender systems. Recently, graph neural networks (GNN) have shown their technical advancements at boosting recommendation performances. Existing GNN-based models mainly focus on aggregation technique and regularization allocation, ignoring the rich entity-aware information hidden in the relation network of KG. In this paper, we explore the relational semantics at the granularity of entities behind a user-item interaction by leveraging knowledge graph, named Entity-aware Collaborative Relation Network (ECRN). Technically, we construct multiple meta-paths from users to entities based on the user-item interaction and item-entity connectivity to obtain user representation, while designing a relation-aware self-attention mechanism to aggregate collaborative signals of items. Empirical results on three benchmarks show that ECRN significantly outperforms state-of-the-art baselines.