Unify Local and Global Information for Top-N Recommendation

Knowledge graph (KG), integrating complex information and containing rich semantics, is widely considered as side information to enhance the recommendation systems. However, most of the existing KG-based methods concentrate on encoding the structural information in the graph, without utilizing the collaborative signals in user-item interaction data, which are important for understanding user preferences. Therefore, the representations learned by these models are insufficient for representing semantic information of users and items in the recommendation environment. The combination of both kinds of data provides a good chance to solve this problem, but it faces the following challenges: i) the inner correlations in user-item interaction data are difficult to capture from one side of the user or item; ii) capturing the knowledge associations on the whole KG would introduce noises and variously influence the recommendation results; iii) the semantic gap between both kinds of data is hard to alleviate.