Session-Based Recommendation with Graph Neural Networks | |
Shu Wu1![]() ![]() ![]() ![]() | |
2019-01-27 | |
会议名称 | 33rd AAAI Conference on Artificial Intelligence / 31st Innovative Applications of Artificial Intelligence Conference / 9th AAAI Symposium on Educational Advances in Artificial Intelligence |
会议日期 | 2019/01/27-2020/02/01 |
会议地点 | Honolulu, HI |
出版者 | AAAI-19 |
摘要 | 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. |
七大方向——子方向分类 | 推荐系统 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57492 |
专题 | 模式识别实验室 |
作者单位 | 1.中国科学院自动化研究所 2.北京科学技术大学 3.同济大学 4.微软亚洲研究院 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Shu Wu,Yuyuan Tang,Yanqiao Zhu,et al. Session-Based Recommendation with Graph Neural Networks[C]:AAAI-19,2019. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Session-Based Recomm(1825KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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