Incorporating metapath interaction on heterogeneous information network for social recommendation

Abstract

Heterogeneous information network (HIN) has recently been widely adopted to describe complex graph structure in recommendation systems, proving its effectiveness in modeling complex graph data. Although existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths, they have the following major limitations. Existing works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths, which are not expressive enough to capture more complicated dependency relationships involved on the metapath. Besides, the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately considered. To tackle these limitations, we propose a novel social recommendation model MPISR, which models MetaPath Interaction for Social Recommendation on heterogeneous information network. Specifically, our model first learns the initial node representation through a pretraining module, and then identifies potential social friends and item relations based on their similarity to construct a unified HIN. We then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different metapaths. Extensive experiments on five real datasets demonstrate the effectiveness of our method.

Graphical abstract

Keywords

heterogeneous information network / social recommender system / metapath interaction / attention mechanism

Yanbin Jiang is currently a postgraduate student in the College of Computer Science and Engineering at Northwest Normal University, China. His general area of research is social recommendation and graph neural networks

Huifang Ma is currently a professor in the College of Computer Science and Engineering at Northwest Normal University, China. She received the BE degree from Northwest Normal University, China in 2003 and the MS degree from Beijing Normal University, China in 2006. She received the PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2010. Her general area of research is data mining and machine learning

Xiaohui Zhang is currently a postgraduate student in the College of Computer Science and Engineering at Northwest Normal University, China. Her general area of research is sequential recommendation and graph neural networks

Zhixin Li is currently a professor in the College of Computer Science & Information Engineering & College of Software, Guangxi Normal University, China. He received his PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2010. His general area of research is natural language processing, machine learning, intelligent recommendation system and formal methods

Liang Chang is a professor in the School of Computer Science and Information Security, Guilin University of Electronic Technology, China. He received his PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2008. His research interest covers data and knowledge engineering, intelligent recommendation system, and formal methods

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 61762078, 62276073, 61966009 and U22A2099), the Industrial Support Project of Gansu Colleges (No. 2022CYZC11), the Natural Science Foundation of Gansu Province (21JR7RA114), the Northwest Normal University Young Teachers Research Capacity Promotion Plan (NWNU-LKQN2019-2), the Industrial Support Project of Gansu Colleges (No. 2022CYZC11), and the Northwest Normal University Post-graduate Research Funding Project (2021KYZZ02107).

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