STAN: Spatio-Temporal Attention Network for Next Point-of-Interest Recommendation | |
Luo, Yingtao1; Liu, Qiang2,3; Liu, Zhaocheng4 | |
2021-04 | |
会议名称 | The Web Conference |
会议日期 | 2021.04.19-2021.04.23 |
会议地点 | Ljubljana, Slovenia |
摘要 | The next location recommendation is at the core of various location-based applications. Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical gridding and model temporal relation with explicit time intervals, while some vital questions remain unsolved. Non-adjacent locations and non-consecutive visits provide non-trivial correlations for understanding a user’s behavior but were rarely considered. To aggregate all relevant visits from user trajectory and recall the most plausible candidates from weighted representations, here we propose a Spatio-Temporal Attention Network (STAN) for location recommendation. STAN explicitly exploits relative spatiotemporal information of all the check-ins with self-attention layers along the trajectory. This improvement allows a point-to-point interaction between non-adjacent locations and non-consecutive check-ins with explicit spatio-temporal effect. STAN uses a bi-layer attention architecture that firstly aggregates spatiotemporal correlation within user trajectory and then recalls the target with consideration of personalized item frequency (PIF). By visualization, we show that STAN is in line with the above intuition. Experimental results unequivocally show that our model outperforms the existing state-of-the-art methods by 9-17%. |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47489 |
专题 | 模式识别实验室 |
通讯作者 | Liu, Qiang |
作者单位 | 1.University of Washington 2.Institute of Automation, Chinese Academy of Sciences 3.University of Chinese Academy of Sciences 4.Renmin University of China |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Luo, Yingtao,Liu, Qiang,Liu, Zhaocheng. STAN: Spatio-Temporal Attention Network for Next Point-of-Interest Recommendation[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
3442381.3449998 (1).(1325KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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