CASIA OpenIR  > 智能感知与计算研究中心
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
专题智能感知与计算研究中心
通讯作者Wu, Shu
作者单位Institute of Automation, Chinese Academy of Sciences
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.
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
Predicting the Next (688KB)会议论文 开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Liu, Qiang]的文章
[Wu, Shu]的文章
[Wang, Liang]的文章
百度学术
百度学术中相似的文章
[Liu, Qiang]的文章
[Wu, Shu]的文章
[Wang, Liang]的文章
必应学术
必应学术中相似的文章
[Liu, Qiang]的文章
[Wu, Shu]的文章
[Wang, Liang]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Predicting the Next Location_ A Recurrent Model with Spatial and Temporal Contexts.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。