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Where you like to go next: successive point-of-interest recommendation

Published: 03 August 2013 Publication History

Abstract

Personalized point-of-interest (POI) recommendation is a significant task in location-based social networks (LBSNs) as it can help provide better user experience as well as enable third-party services, e.g., launching advertisements. To provide a good recommendation, various research has been conducted in the literature. However, pervious efforts mainly consider the "check-ins" in a whole and omit their temporal relation. They can only recommend POI globally and cannot know where a user would like to go tomorrow or in the next few days. In this paper, we consider the task of successive personalized POI recommendation in LBSNs, which is a much harder task than standard personalized POI recommendation or prediction. To solve this task, we observe two prominent properties in the check-in sequence: personalized Markov chain and region localization. Hence, we propose a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions. Our proposed FPMC-LR not only exploits the personalized Markov chain in the check-in sequence, but also takes into account users' movement constraint, i.e., moving around a localized region. More importantly, utilizing the information of localized regions, we not only reduce the computation cost largely, but also discard the noisy information to boost recommendation. Results on two real-world LBSNs datasets demonstrate the merits of our proposed FPMC-LR.

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    cover image Guide Proceedings
    IJCAI '13: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
    August 2013
    3266 pages
    ISBN:9781577356332

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    • The International Joint Conferences on Artificial Intelligence, Inc. (IJCAI)

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    AAAI Press

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    Published: 03 August 2013

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    • (2024)Where Have You Been? A Study of Privacy Risk for Point-of-Interest RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671758(175-186)Online publication date: 25-Aug-2024
    • (2023)Theoretically guaranteed bidirectional data rectification for robust sequential recommendationProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666249(2850-2876)Online publication date: 10-Dec-2023
    • (2023)Next POI recommendation with dynamic graph and explicit dependencyProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25608(4827-4834)Online publication date: 7-Feb-2023
    • (2023)Learning Hierarchical Spatial Tasks with Visiting Relations for Next POI RecommendationACM Transactions on Recommender Systems10.1145/36105841:4(1-26)Online publication date: 27-Jul-2023
    • (2023)Pre-Training Across Different Cities for Next POI RecommendationACM Transactions on the Web10.1145/360555417:4(1-27)Online publication date: 10-Oct-2023
    • (2023)Modeling Long- and Short-Term User Preferences via Self-Supervised Learning for Next POI RecommendationACM Transactions on Knowledge Discovery from Data10.1145/359721117:9(1-20)Online publication date: 15-Jun-2023
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