Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation.

A recommendation engine that relies solely on interactions between users and items will be limited in its ability to provide accurate, diverse and explanation-rich recommendations. Side information should be taken into account to improve performance. Methods like Factorisation Machines (FM) cast recommendation as a supervised learning problem, where each interaction is viewed as an independent instance with side information encapsulated. Previous studies in top-K recommendation have incorporated knowledge graphs (KG) into the recommender system to provide rich information about the relationships between users, items and entities. Nevertheless, these studies do not explicitly capture the preference of users for the side information. Furthermore, some studies explain the recommendation, but there is no unified method of measuring explanation quality. In this work, we investigate the utility of Graph Convolutional Networks (GCN) and multi-task learning techniques to capture the tripartite relations between users, items and entities. Based on our study, we propose that in the hybrid structure of the KG, its rich relationships are an essential factor for successful recommendation from both an explanation and performance perspective. We propose a novel method named Light Knowledge Graph Convolutional Network (LKGCN) which explicitly models the high-order connectivities between user items and entities. Specifically, we use multi-task learning techniques and attention mechanisms in order to combine user preferences on items and entities. Additionally, we present a unified evaluation method PeX for explainable recommendation models. Extensive experiments on real-world datasets show that the LKGCN is conceptually superior to existing graph-based recommendation methods from two perspectives: recommendation accuracy and interpretation. We release the codes and datasets on github(1).