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Location prediction over sparse user mobility traces using RNNs: Flashback in hidden states!
Dingqi Yang1,3; Benjamin Fankhauser2; Paolo Rosso1; Philippe Cudre-Mauroux1
2020
Conference NameInternational Joint Conference on Artificial Intelligence (IJCAI-20)
Source PublicationProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20)
Volume2021-January
Pages2184-2190
Conference DateJanuary 7th until January 15th, 2021
Conference Placevirtual Japanese reality
Abstract

Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by considering spatiotemporal contexts. The most adopted scheme is to incorporate spatiotemporal factors into the recurrent hidden state passing process of RNNs using context-parameterized transition matrices or gates. However, such a scheme oversimplifies the temporal periodicity and spatial regularity of user mobility, and thus cannot fully benefit from rich historical spatiotemporal contexts encoded in user mobility traces. Against this background, we propose Flashback, a general RNN architecture designed for modeling sparse user mobility traces by doing flashbacks on hidden states in RNNs. Specifically, Flashback explicitly uses spatiotemporal contexts to search past hidden states with high predictive power (i.e., historical hidden states sharing similar contexts as the current one) for location prediction, which can then directly benefit from rich spatiotemporal contexts. Our extensive evaluation compares Flashback against a sizable collection of state-of-the-art techniques on two real-world LBSN datasets. Results show that Flashback consistently and significantly outperforms state-of-the-art RNNs involving spatiotemporal factors by 15.9% to 27.6% in the next location prediction task.

DOI10.24963/ijcai.2020/302
URLView the original
Language英語English
Scopus ID2-s2.0-85097331203
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Document TypeConference paper
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Affiliation1.University of Fribourg,Switzerland
2.University of Bern,Switzerland
3.University of Macau,Macao
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Dingqi Yang,Benjamin Fankhauser,Paolo Rosso,et al. Location prediction over sparse user mobility traces using RNNs: Flashback in hidden states![C],2020:2184-2190.
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