Abstract:
Since it can effectively address the problem of sparsity and cold start of collaborative filtering, knowledge graph (KG) is widely studied and employed as side information in the field of recommender systems. However, most of existing KG-based recommendation methods mainly focus on how to effectively encode the knowledge associations in KG, without highlighting the crucial collaborative signals which are latent in user-item interactions. As such, the learned embeddings underutilize the two kinds of pivotal information and are insufficient to effectively represent the latent semantics of users and items in vector space.In this paper, we propose a novel method named Collaborative Knowledge-aware Attentive Network (CKAN) which explicitly encodes the collaborative signals by collaboration propagation and proposes a natural way of combining collaborative signals with knowledge associations together. Specifically, CKAN employs a heterogeneous propagation strategy to explicitly encode both kinds of information, and applies a knowledge-aware attention mechanism to discriminate the contribution of different knowledge-based neighbors. Compared with other KG-based methods, CKAN provides a brand-new idea of combining collaborative information with knowledge information together. We apply the proposed model on four real-world datasets, and the empirical results demonstrate that CKAN significantly outperforms several compelling state-of-the-art baselines.
