research-article

Location recommendation in location-based social networks using user check-in data

Authors:

Manolis Terrovitis,

Nikos MamoulisAuthors Info & Claims

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

Pages 374 - 383

https://doi.org/10.1145/2525314.2525357

Published: 05 November 2013 Publication History

Abstract

This paper studies the problem of recommending new venues to users who participate in location-based social networks (LBSNs). As an increasingly larger number of users partake in LBSNs, the recommendation problem in this setting has attracted significant attention in research and in practical applications. The detailed information about past user behavior that is traced by the LBSN differentiates the problem significantly from its traditional settings. The spatial nature in the past user behavior and also the information about the user social interaction with other users, provide a richer background to build a more accurate and expressive recommendation model.

Although there have been extensive studies on recommender systems working with user-item ratings, GPS trajectories, and other types of data, there are very few approaches that exploit the unique properties of the LBSN user check-in data. In this paper, we propose algorithms that create recommendations based on four factors: a) past user behavior (visited places), b) the location of each venue, c) the social relationships among the users, and d) the similarity between users. The proposed algorithms outperform traditional recommendation algorithms and other approaches that try to exploit LBSN information.

To design our recommendation algorithms we study the properties of two real LBSNs, Brightkite and Gowalla, and analyze the relation between users and visited locations. An experimental evaluation using data from these LBSNs shows that the exploitation of the additional geographical and social information allows our proposed techniques to outperform the current state of the art.

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Cited By

Mishra MKala SAmano TYamaguchi H(2024)Balancing Privacy and Planning: Using Counterfactuals to Predict and Optimize Tourism in Wakayama CityProceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies10.1145/3681768.3698504(25-30)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681768.3698504
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
Acharya MMohbey K(2024)Exploring the evolution, progress, and future of point-of-interest recommendation over location-based social network: a comprehensive reviewGeoInformatica10.1007/s10707-024-00531-xOnline publication date: 28-Oct-2024
https://doi.org/10.1007/s10707-024-00531-x
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Index Terms

Location recommendation in location-based social networks using user check-in data
1. Information systems
  1. Information systems applications

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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.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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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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EU/Greece funded COOPERATION: MoDiSSENSE Project
Research Grants Council, University Grants Committee, Hong Kong

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SIGSPATIAL'13

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SIGSPATIAL

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 220 of 1,116 submissions, 20%

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Cited By

Mishra MKala SAmano TYamaguchi H(2024)Balancing Privacy and Planning: Using Counterfactuals to Predict and Optimize Tourism in Wakayama CityProceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies10.1145/3681768.3698504(25-30)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681768.3698504
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
Acharya MMohbey K(2024)Exploring the evolution, progress, and future of point-of-interest recommendation over location-based social network: a comprehensive reviewGeoInformatica10.1007/s10707-024-00531-xOnline publication date: 28-Oct-2024
https://doi.org/10.1007/s10707-024-00531-x
Shah PKarmarkar ODholakia DSrivastava K(2024)Safarnaama: User Experience-Based Travel Recommendation SystemData Management, Analytics and Innovation10.1007/978-981-97-3242-5_18(253-271)Online publication date: 23-Jul-2024
https://doi.org/10.1007/978-981-97-3242-5_18
Tian JZhao ZDing Z(2023)Next Point-of-Interest Recommendation Based on Joint Mining of Spatial–Temporal and Semantic Sequential PatternsISPRS International Journal of Geo-Information10.3390/ijgi1207029712:7(297)Online publication date: 24-Jul-2023
https://doi.org/10.3390/ijgi12070297
Kasgari ASafavi SNouri MHou JSarshar NRanjbarzadeh R(2023)Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural NetworkBioengineering10.3390/bioengineering1004049510:4(495)Online publication date: 20-Apr-2023
https://doi.org/10.3390/bioengineering10040495
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
https://dl.acm.org/doi/10.1145/3589132.3625628
Tsai YLuo JWang YChen JZimmermann RShirmohammadi SGriwodz CHefeeda MWang M(2023)The World is Too Big to Download: 3D Model Retrieval for World-Scale Augmented RealityProceedings of the 14th Conference on ACM Multimedia Systems10.1145/3587819.3590970(14-26)Online publication date: 7-Jun-2023
https://dl.acm.org/doi/10.1145/3587819.3590970
Wang CYuan MZhang RPeng KLiu L(2023)Efficient Point-of-Interest Recommendation Services With Heterogenous Hypergraph EmbeddingIEEE Transactions on Services Computing10.1109/TSC.2022.318703816:2(1132-1143)Online publication date: 1-Mar-2023
https://doi.org/10.1109/TSC.2022.3187038
Pan XCai XSong KBaker TGadekallu TYuan X(2023)Location Recommendation Based on Mobility Graph With Individual and Group InfluencesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314986924:8(8409-8420)Online publication date: Aug-2023
https://doi.org/10.1109/TITS.2022.3149869
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