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
Conference NameAAAI Conference on Artificial Intelligence (AAAI)
Source PublicationIn Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI), 2016
Conference DateFebruary 12–17
Conference PlacePhoenix
AbstractSpatial 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.
KeywordContextual Information Recurrent Neural Networks
Document Type会议论文
Corresponding AuthorWu, Shu
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
Predicting the Next (688KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Liu, Qiang]'s Articles
[Wu, Shu]'s Articles
[Wang, Liang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Qiang]'s Articles
[Wu, Shu]'s Articles
[Wang, Liang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Qiang]'s Articles
[Wu, Shu]'s Articles
[Wang, Liang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Predicting the Next Location_ A Recurrent Model with Spatial and Temporal Contexts.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.