research-article

What Your Next Check-in Might Look Like: Next Check-in Behavior Prediction

Authors:

Hui XiongAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 6

Article No.: 112, Pages 1 - 21

https://doi.org/10.1145/3625234

Published: 14 November 2023 Publication History

Abstract

In recent years, the next-POI recommendation has become a trending research topic in the field of trajectory data mining. For protection of user privacy, users’ complete GPS trajectories are difficult to obtain. The check-in information posted by users on social networks has become an important data source for Spatio-temporal Trajectory research. However, state-of-the-art methods neglect the social meaning and the information dissemination function of check-in behavior. The social meaning is an important reason why users are willing to post check-in on social networks, and the information dissemination function means, users can affect each other’s behavior by check-ins. The above characteristics of the check-in behavior make it different from the visiting behavior. We consider a new problem of predicting the next check-in behavior including the check-in time, the POI (point-of-interest) where the check-in is located, functional semantics of the POI, and so on. To solve the proposed problem, we build a multi-task learning model called DPMTM, and a pre-training module is designed to extract dynamic social semantics of check-in behaviors. Our results show that the DPMTM model works well in the check-in behavior problem.

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Index Terms

What Your Next Check-in Might Look Like: Next Check-in Behavior Prediction
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 6

December 2023

493 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3632517

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 14 November 2023

Online AM: 29 September 2023

Accepted: 23 August 2023

Revised: 27 May 2023

Received: 25 October 2022

Published in TIST Volume 14, Issue 6

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National Key RD Program of China
National Science Foundation of China
Science Foundation of Distinguished Young Scholars of Shaanxi
Key Research and Development Program of Shaanxi
Big Mobility Rhino-bird Special Research Program of Tencent
Innovation Capability Support Plan of Shaanxi

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Santos GLima K(2024)Impact of combinatorial optimization on reinforcement learning for stock trading in financial marketsProceedings of the 20th Brazilian Symposium on Information Systems10.1145/3658271.3658282(1-8)Online publication date: 23-May-2024
https://doi.org/10.1145/3658271.3658282
Zhang WZhao LXia HSun SSun JQin MLi XZhao YZhao YCai XZheng LWang XAn BBaeza-Yates RBonchi F(2024)A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and GeneralistProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671801(4314-4325)Online publication date: 25-Aug-2024
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