Session-Based Recommendation with Graph Neural Networks


  • Shu Wu Institute of Automation, Chinese Academy of Sciences
  • Yuyuan Tang University of Science & Technology Beijing
  • Yanqiao Zhu Tongji University
  • Liang Wang Institute of Automation, Chinese Academy of Sciences
  • Xing Xie Microsoft Research Asia
  • Tieniu Tan Institute of Automation, Chinese Academy of Sciences



The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user vectors in sessions and neglect complex transitions of items. To obtain accurate item embedding and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are modeled as graphstructured data. Based on the session graph, GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods. Each session is then represented as the composition of the global preference and the current interest of that session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.




How to Cite

Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019). Session-Based Recommendation with Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 346-353.



AAAI Technical Track: AI and the Web