research-article

Exploring temporal effects for location recommendation on location-based social networks

Authors:

Huan LiuAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 93 - 100

https://doi.org/10.1145/2507157.2507182

Published: 12 October 2013 Publication History

Abstract

Location-based social networks (LBSNs) have attracted an inordinate number of users and greatly enriched the urban experience in recent years. The availability of spatial, temporal and social information in online LBSNs offers an unprecedented opportunity to study various aspects of human behavior, and enable a variety of location-based services such as location recommendation. Previous work studied spatial and social influences on location recommendation in LBSNs. Due to the strong correlations between a user's check-in time and the corresponding check-in location, recommender systems designed for location recommendation inevitably need to consider temporal effects. In this paper, we introduce a novel location recommendation framework, based on the temporal properties of user movement observed from a real-world LBSN dataset. The experimental results exhibit the significance of temporal patterns in explaining user behavior, and demonstrate their power to improve location recommendation performance.

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Cited By

Liu JGao HYang CShi CYang TCheng HXie QWang XWang D(2025)Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest RecommendationTsinghua Science and Technology10.26599/TST.2023.901014830:1(186-197)Online publication date: Feb-2025
https://doi.org/10.26599/TST.2023.9010148
Sang CYang YZhang YLiao S(2025)A user preference knowledge graph incorporating spatio-temporal transfer features for next POI recommendationApplied Intelligence10.1007/s10489-025-06290-y55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-025-06290-y
Yu FTong FZhao DXu LLiu N(2025)Multi-Interest Granularity Guided Semi-Joint Learning for N-Successive POI RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_9(131-146)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_9
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Index Terms

Exploring temporal effects for location recommendation on location-based social networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 ACM.

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Published: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

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RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Liu JGao HYang CShi CYang TCheng HXie QWang XWang D(2025)Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest RecommendationTsinghua Science and Technology10.26599/TST.2023.901014830:1(186-197)Online publication date: Feb-2025
https://doi.org/10.26599/TST.2023.9010148
Sang CYang YZhang YLiao S(2025)A user preference knowledge graph incorporating spatio-temporal transfer features for next POI recommendationApplied Intelligence10.1007/s10489-025-06290-y55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-025-06290-y
Yu FTong FZhao DXu LLiu N(2025)Multi-Interest Granularity Guided Semi-Joint Learning for N-Successive POI RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_9(131-146)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_9
Yu HCheng Z(2024)AST-PG: Attention-Based Spatial–Temporal Point-of-Interest-Group Model for Real-Time Point-of-Interest RecommendationApplied Sciences10.3390/app1412533714:12(5337)Online publication date: 20-Jun-2024
https://doi.org/10.3390/app14125337
Züfle APfoser DWenk CCrooks AKavak HAnderson TKim JHolt NDiantonio A(2024)In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)ACM Transactions on Spatial Algorithms and Systems10.1145/367255710:2(1-27)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3672557
Xu PLiu JWu L(2024)Joint Spatio-Temporal Modeling and Generative Adversarial Network for Point-of-Interest Recommendation2024 International Conference on Electronics and Devices, Computational Science (ICEDCS)10.1109/ICEDCS64328.2024.00032(155-160)Online publication date: 23-Sep-2024
https://doi.org/10.1109/ICEDCS64328.2024.00032
Wang YChen HLiu SWang KHu Y(2024)Geo-aware graph-augmented self-attention network for individual mobility predictionFuture Generation Computer Systems10.1016/j.future.2023.09.021151(1-11)Online publication date: Feb-2024
https://doi.org/10.1016/j.future.2023.09.021
Li ZShao X(2024)Future locations prediction with multi-graph attention networks based on spatial–temporal LSTM frameworkThe Journal of Supercomputing10.1007/s11227-024-06249-980:14(20020-20041)Online publication date: 29-May-2024
https://dl.acm.org/doi/10.1007/s11227-024-06249-9
Yi KYamagishi RLi JLi TBai ZMa Q(2024)User-experience oriented POI recommendation with pseudo ratingMultimedia Tools and Applications10.1007/s11042-024-19455-7Online publication date: 28-Jun-2024
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Zou YZhao K(2024)Short-term POI recommendation with personalized time-weighted latent rankingDiscover Computing10.1007/s10791-024-09450-927:1Online publication date: 3-Jul-2024
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