Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts | |
Liu, Qiang; Wu, Shu; Wang, Liang; Tan, Tieniu | |
2016 | |
会议名称 | AAAI Conference on Artificial Intelligence (AAAI) |
会议录名称 | In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI), 2016 |
会议日期 | February 12–17 |
会议地点 | Phoenix |
摘要 | Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more temporal and spatial contextual information is collected in systems, and the location prediction problem becomes crucial and feasible. Some works have been proposed to address this problem, but they all have their limitations. Factorizing Personalized Markov Chain (FPMC) is constructed based on a strong independence assumption among different factors, which limits its performance. Tensor Factorization (TF) faces the cold start problem in predicting future actions. Recurrent Neural Networks (RNN) model shows promising performance comparing with PFMC and TF, but all these methods have problem in modeling continuous time interval and geographical distance. In this paper, we extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN). ST-RNN can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transition matrices for different geographical distances. Experimental results show that the proposed ST-RNN model yields significant improvements over the competitive compared methods on two typical datasets, i.e., Global Terrorism Database (GTD) and Gowalla dataset. |
关键词 | Contextual Information Recurrent Neural Networks |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/12329 |
专题 | 模式识别实验室 |
通讯作者 | Wu, Shu |
作者单位 | Institute of Automation, Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
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 | Liu, Qiang,Wu, Shu,Wang, Liang,et al. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts[C],2016. |
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