TKGAT: Graph attention network for knowledge-enhanced tag-aware recommendation system

In recent practices, sparsity problems often arise in recommendation systems, resulting in weak generalization ability. To alleviate this problem, tag-aware recommendation systems (TRS) leverage personalized tags to enhance the modeling of user preferences and item characteristics. However, current tag-aware methods suffer from arbitrary user behaviors as they limit the additional information only to user tags. In this paper, we investigate a more general scenario, namely Knowledge-enhanced Tag-aware Recommendation System (KTRS) which involves auxiliary knowledge compared with TRS. Correspondingly, we propose a novel recommendation model for KTRS, called TKGAT. It firstly constructs a collaborative recommendation graph and then learns heterogeneous representation via an multi-layer multi-head attention mechanism. Experiments conducted on real-world datasets demonstrate that the proposed system outperforms the state-of-the-art recommendation methods, and show effectiveness of the auxiliary knowledge.