HAGERec: Hierarchical Attention Graph Convolutional Network Incorporating Knowledge Graph for Explainable Recommendation

Knowledge graph (KG) can provide auxiliary information for recommender system to alleviate the sparsity and cold start problems, while graph convolutional networks (GCN) has recently been established as the state-of-the-art representation learning method. The combination of them is a promising perspective to improve the performance of graph-structured recommendation. However, most of GCN-based recommendations focus on homogeneous graph or user/item-similarity graph, fail to fully make use of the complex and rich semantics between entities in heterogeneous knowledge graph. In this paper, we develop Hierarchical Attention Graph Convolutional Network Incorporating Knowledge Graph for Explainable Recommendation (HAGERec) to explore users' potential preferences from the high-order connectivity structure of heterogeneous knowledge graph. To exploit semantic information, HAGERec simultaneously learn the representations of users and items via a bi-directional information propagation strategy. Specifically, the entity's representation can be aggregated through messages passing from its local proximity structure, and a hierarchical attention mechanism is developed to adaptively characterize and adjust collaborative signals. With the help of the attention mechanism, an attentive entity sampling strategy is proposed to select relevant neighbor entities, and the explainability is endowed to the model by building knowledge-aware connectivity. Experiments conducted on four real-world public datasets demonstrate the state-of-the-art performance and the strong explainability of HAGERec. (C) 2020 Elsevier B.V. All rights reserved.