Graph Heterogeneous Multi-Relational Recommendation

Traditional studies on recommender systems usually leverage only one type of user behaviors (the optimization target, such as purchase), despite the fact that users also generate a large number of various types of interaction data (e. g. , view, click, add-to-cart, etc). Generally, these heterogeneous multirelational data provide well-structured information and can be used for high-quality recommendation. Early efforts towards leveraging these heterogeneous data fail to capture the high-hop structure of user-item interactions, which are unable to make full use of them and may only achieve constrained recommendation performance. In this work, we propose a new multi-relational recommendation model named Graph Heterogeneous Collaborative Filtering (GHCF). To explore the high-hop heterogeneous user-item interactions, we take the advantages of Graph Convolutional Network (GCN) and further improve it to jointly embed both representations of nodes (users and items) and relations for multi-relational prediction. Moreover, to fully utilize the whole heterogeneous data, we perform the advanced efficient non-sampling optimization under a multi-task learning framework. Experimental results on two public benchmarks show that GHCF significantly outperforms the state-of-the-art recommendation methods, especially for cold-start users who have few primary item interactions. Further analysis verifies the importance of the proposed embedding propagation for modelling high-hop heterogeneous user-item interactions, showing the rationality and effectiveness of GHCF. Our implementation has been released (https: //github. com/chenchongthu/GHCF).

Authors

• Chong Chen Terminus Group
• Weizhi Ma Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China
• Min Zhang College of Intelligence and Computing, Tianjin University
• Zhaowei Wang
• Xiuqiang He
• Chenyang Wang School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
• Yiqun Liu Tsinghua University
• Shaoping Ma

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