Preference-aware Heterogeneous Graph Neural Networks for Recommendation

Yao Fu, Junhong Wan, Hong Zhao, Weihao Jiang, Shiliang Pu

Abstract: Knowledge graph is considered as a significant information structure for recommendation, therefore the graph based recommendation has attracted increasing attention in recent years. However, the existing methods face two major challenges. First, the users' preferences should be well considered in the algorithmic model and explicitly shown after model training. Second, there need a better solution to simultaneously learn and combine the information on multiple graphs from different aspects rather than the methods designed for single homogeneous graph. In this paper, we propose the preferences embeddings, which are able to learn the explicit representations for the preferences that influence the users' choices. Further, we innovatively design three channels in a new graph neural network that contains different graph convolutions specifically for the recommendation scenario. This framework can effectively excavate and combine heterogeneous information among user graph, item graph and interaction graph. Extensive experiments on real-world datasets demonstrate the effectiveness and good interpretability of the proposed framework.