Community Enhanced Knowledge Graph for Recommendation

Due to the capability of encoding auxiliary information for alleviating the data sparsity issue, knowledge graph (KG) has gained an increasing amount of attention in recent years. With auxiliary knowledge about items, the KG-based recommender systems have achieved better performance compared with the existing methods. However, the effectiveness of the KG-based methods highly depends on the quality of the KG. Unfortunately, KGs are usually with the problem of incompleteness and sparseness. Besides, the existing KG-based methods could not discriminate the importance of different factors that users consider when making decisions, which may degrade the interpretability of the methods. In this article, we propose a recommendation model named community enhanced knowledge graph for recommendation (CEKGR). By adding entities and relations, the KG is enriched with more semantic information, which would help mine users' preference for better recommendation. With weights of each path, the interpretability of the recommendation can be improved. To validate the effectiveness of the proposed method, we conduct experiments on three public datasets. Experiment results have shown the improvement compared with other state-of-the-art methods. Besides, case study has illustrated the interpretability of the proposed recommendation model.