research-article

iGSLR: personalized geo-social location recommendation: a kernel density estimation approach

Authors:

Jia-Dong Zhang,

Chi-Yin ChowAuthors Info & Claims

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 334 - 343

https://doi.org/10.1145/2525314.2525339

Published: 05 November 2013 Publication History

Abstract

With the rapidly growing location-based social networks (LBSNs), personalized geo-social recommendation becomes an important feature for LBSNs. Personalized geo-social recommendation not only helps users explore new places but also makes LBSNs more prevalent to users. In LBSNs, aside from user preference and social influence, geographical influence has also been intensively exploited in the process of location recommendation based on the fact that geographical proximity significantly affects users' check-in behaviors. Although geographical influence on users should be personalized, current studies only model the geographical influence on all users' check-in behaviors in a universal way. In this paper, we propose a new framework called iGSLR to exploit personalized social and geographical influence on location recommendation. iGSLR uses a kernel density estimation approach to personalize the geographical influence on users' check-in behaviors as individual distributions rather than a universal distribution for all users. Furthermore, user preference, social influence, and personalized geographical influence are integrated into a unified geo-social recommendation framework. We conduct a comprehensive performance evaluation for iGSLR using two large-scale real data sets collected from Foursquare and Gowalla which are two of the most popular LBSNs. Experimental results show that iGSLR provides significantly superior location recommendation compared to other state-of-the-art geo-social recommendation techniques.

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Cited By

Huang WLiu BWang Z(2024)A Metric Learning Perspective on the Implicit Feedback-Based Recommendation Data Imbalance ProblemElectronics10.3390/electronics1302041913:2(419)Online publication date: 19-Jan-2024
https://doi.org/10.3390/electronics13020419
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
Kwon JAhn SSeo YHong JPark J(2024)RecKG: Knowledge Graph for Recommender SystemsProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636009(600-607)Online publication date: 8-Apr-2024
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Index Terms

iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Spatial-temporal systems

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Published In

cover image ACM Conferences

SIGSPATIAL'13: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2013

598 pages

ISBN:9781450325219

DOI:10.1145/2525314

General Chairs:
Craig Knoblock
University of Southern California
,
Markus Schneider
University of Florida
,
Program Chairs:
Peer Kröger
Ludwig-Maximilians-Universität München, Germany
,
John Krumm
Microsoft Research
,
Peter Widmayer
ETH Zürich, Switzerland

Copyright © 2013 ACM.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2013

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SIGSPATIAL'13

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SIGSPATIAL'13: 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2013

Florida, Orlando

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

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https://doi.org/10.3390/electronics13020419
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
Kwon JAhn SSeo YHong JPark J(2024)RecKG: Knowledge Graph for Recommender SystemsProceedings of the 39th ACM/SIGAPP Symposium on Applied Computing10.1145/3605098.3636009(600-607)Online publication date: 8-Apr-2024
https://dl.acm.org/doi/10.1145/3605098.3636009
An JLi GJiang W(2024)NRDL: Decentralized user preference learning for privacy-preserving next POI recommendationExpert Systems with Applications10.1016/j.eswa.2023.122421239(122421)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122421
Mat ASaran A(2024)Enhancing session-based trip recommendations using matrix factorization: a study on algorithm efficiency and resource utilizationThe Journal of Supercomputing10.1007/s11227-024-06726-181:1Online publication date: 14-Dec-2024
https://doi.org/10.1007/s11227-024-06726-1
Chaudhuri DMajumder ACowlessur SShankar A(2024)Time-Slot-Based POI Recommendations Using User’s Current LocationProceedings of International Conference on Advanced Communications and Machine Intelligence10.1007/978-981-97-6222-4_45(547-556)Online publication date: 6-Dec-2024
https://doi.org/10.1007/978-981-97-6222-4_45
An JJiang WLi G(2023)Bidirectional Trust-Enhanced Collaborative Filtering for Point-of-Interest RecommendationSensors10.3390/s2308414023:8(4140)Online publication date: 20-Apr-2023
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Khan NWan WRiaz RJiang SWang X(2023)Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network DataApplied Sciences10.3390/app1306351713:6(3517)Online publication date: 9-Mar-2023
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Huang WLiu BWang Z(2023)Paper Recommendation via Correlation Pattern Mining and Attention MechanismJournal of Sensors10.1155/2023/33113632023:1Online publication date: 18-Oct-2023
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Qin YGao CTu ZWu HWei SWang YZhang LLi YRenz MNascimento M(2023)Modeling Multi-Grained User Preference in Location VisitationProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625628(1-10)Online publication date: 13-Nov-2023
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