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Time-aware location sequence recommendation for cold-start mobile users

Authors:

Ting Shen,

Haiquan Chen,

Wei-Shinn KuAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 484 - 487

https://doi.org/10.1145/3274895.3274958

Published: 06 November 2018 Publication History

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Abstract

In this paper, we study the problem of recommending time-sensitive location sequence for mobile users using their check-in data on location-based social networks. Most of the existing studies on Point of Interest (POI) recommendation and prediction fail to address the following two key challenges: (1) how to handle the scenario where the user-location matrix is very sparse (i.e., each user has a very limited number of check-ins, or to say, cold-start users), and (2) how to recommend an optimal time-sensitive visit sequence where each venue matches a time slot specified by users, based on their check-in histories. Motivated by the two challenges above, we propose a predictive framework that enables time-sensitive location sequence recommendation leveraging both the users' semantic and spatial similarities, especially for cold-start users. Our novel framework consists of three modules: semantic similarity modeling, spatial similarity modeling, and on-line sequence recommendation. In semantic modeling, we calculate users' similarity scores by comparing users' temporal hierarchical semantic trees. In spatial modeling, we use Gaussian Mixture Model (GMM) to compute users' similarity scores with respect to their geographical movement paterns. Aferwards, we combine the check-in data of the target user with those of her top-k most similar users in terms of both semantic and spatial similarities to train a personalized Hidden Markov Model (HMM) to predict the most probable venue category for each specified time slot. At last, we recommend location sequence based on the predicted venue category sequence for the target user using geographical mapping.

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https://doi.org/10.1109/ICDE55515.2023.00053
Viktoratos ITsadiras A(2022)A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising SystemsAlgorithms10.3390/a1503007215:3(72)Online publication date: 22-Feb-2022
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Wei XLiu CLiu YLi YZhang K(2022)Next location recommendation: a multi-context features integration perspectiveWorld Wide Web10.1007/s11280-022-01126-y26:4(2051-2074)Online publication date: 16-Dec-2022
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Index Terms

Time-aware location sequence recommendation for cold-start mobile users
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Location based services

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Information & Contributors

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Published In

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2018

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National Science Foundation

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SIGSPATIAL '18

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SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

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SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

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https://doi.org/10.1109/ICDE55515.2023.00053
Viktoratos ITsadiras A(2022)A Machine Learning Approach for Solving the Frozen User Cold-Start Problem in Personalized Mobile Advertising SystemsAlgorithms10.3390/a1503007215:3(72)Online publication date: 22-Feb-2022
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https://doi.org/10.1007/s11280-022-01126-y
Pirayesh JChen HQin XKu WYan DDemartini GZuccon GCulpepper JHuang ZTong H(2021)MentalSpotProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482366(1437-1446)Online publication date: 26-Oct-2021
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Mo FYamana H(2019)Point of Interest Recommendation by Exploiting Geographical Weighted Center and Categorical Preference2019 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2019.00021(73-76)Online publication date: Nov-2019
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