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Session-Based Recommendation with Graph Neural Networks
Shu Wu1; Yuyuan Tang2; Yanqiao Zhu3; Liang Wang1; Xing Xie4; Tieniu Tan1
Conference Name33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence
Conference Date2019/01/27-2020/02/01
Conference PlaceHonolulu, HI

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.

Sub direction classification推荐系统
planning direction of the national heavy laboratory智能计算与学习
Paper associated data
Document Type会议论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shu Wu,Yuyuan Tang,Yanqiao Zhu,et al. Session-Based Recommendation with Graph Neural Networks[C]:AAAI-19,2019.
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