research-article

Where Could We Go?: Recommendations for Groups in Location-Based Social Networks

Authors:

Frederick Ayala-Gómez,

Bálint Daróczy,

Michael Mathioudakis,

András Benczúr,

Aristides GionisAuthors Info & Claims

WebSci '17: Proceedings of the 2017 ACM on Web Science Conference

Pages 93 - 102

https://doi.org/10.1145/3091478.3091485

Published: 25 June 2017 Publication History

Abstract

Location-Based Social Networks (LBSNs) enable their users to share with their friends the places they go to and whom they go with. Additionally, they provide users with recommendations for Points of Interest (POI) they have not visited before. This functionality is of great importance for users of LBSNs, as it allows them to discover interesting places in populous cities that are not easy to explore. For this reason, previous research has focused on providing recommendations to LBSN users. Nevertheless, while most existing work focuses on recommendations for individual users, techniques to provide recommendations to groups of users are scarce.

In this paper, we consider the problem of recommending a list of POIs to a group of users in the areas that the group frequents. Our data consist of activity on Swarm, a social networking app by Foursquare, and our results demonstrate that our proposed Geo-Group-Recommender (GGR), a class of hybrid recommender systems that combine the group geographical preferences using Kernel Density Estimation, category and location features and group check-ins outperform a large number of other recommender systems. Moreover, we find evidence that user preferences differ both in venue category and in location between individual and group activities. We also show that combining individual recommendations using group aggregation strategies is not as good as building a profile for a group. Our experiments show that (GGR) outperforms the baselines in terms of precision and recall at different cutoffs.

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Cited By

Turno FVesjolijs AKijonoka JYatskiv Jackiva I(2024)Context-Aware Personalized Route Recommendations for Bicycle Users Using Large-Scale Mobility Data2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00068(449-454)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00068
Alves PMartins ANegrão FNovais PAlmeida AMarreiros G(2024)Are heterogeinity and conflicting preferences no longer a problem? Personality-based dynamic clustering for group recommender systemsExpert Systems with Applications10.1016/j.eswa.2024.124812255(124812)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124812
Turno FJackiva I(2023)Graph-Based Approach for Personalized Travel RecommendationsTransport and Telecommunication Journal10.2478/ttj-2023-003324:4(423-433)Online publication date: 17-Nov-2023
https://doi.org/10.2478/ttj-2023-0033
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Index Terms

Where Could We Go?: Recommendations for Groups in Location-Based Social Networks
1. Information systems

Recommendations

Location recommendation for out-of-town users in location-based social networks
CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Most previous research on location recommendation services in location-based social networks (LBSNs) makes recommendations without considering where the targeted user is currently located. Such services may recommend a place near her hometown even if ...
Collaborative group-activity recommendation in location-based social networks
GeoCrowd '14: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information

Location-based social networks (LBSNs) such as Foursquare, Google+ Local have become a popular platform for users to share their activities with family and friends. They provide rich information for us to study research issues of group recommendation ...
A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable ...

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Information & Contributors

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Published In

cover image ACM Conferences

WebSci '17: Proceedings of the 2017 ACM on Web Science Conference

June 2017

438 pages

ISBN:9781450348966

DOI:10.1145/3091478

Conference Chairs:
Peter Fox
Rensselaer Polytechnic Institute, USA
,
Deborah McGuinness
Rensselaer Polytechnic Institute, USA
,
Lindsay Poirer
Rensselaer Polytechnic Institute, USA
,
Program Chairs:
Paolo Boldi
Universita degli Studi di Milano, Italy
,
Katharina Kinder-Kurlanda
GESIS - Leibniz Institute for the Social Sciences, Germany

Copyright © 2017 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2017

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Research-article

Funding Sources

Research and Technology Innovation Fund
European Institute of Innovation and Technology
Hungarian Academy of Sciences
Academy of Finland project "Nestor"
EC H2020 RIA project "SoBigData"
Mexican National Council for Science and Technology

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WebSci '17

Sponsor:

SIGWEB

WebSci '17: ACM Web Science Conference

June 25 - 28, 2017

New York, Troy, USA

Acceptance Rates

WebSci '17 Paper Acceptance Rate 30 of 85 submissions, 35%;

Overall Acceptance Rate 245 of 933 submissions, 26%

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Cited By

Turno FVesjolijs AKijonoka JYatskiv Jackiva I(2024)Context-Aware Personalized Route Recommendations for Bicycle Users Using Large-Scale Mobility Data2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00068(449-454)Online publication date: 2-Jul-2024
https://doi.org/10.1109/COMPSAC61105.2024.00068
Alves PMartins ANegrão FNovais PAlmeida AMarreiros G(2024)Are heterogeinity and conflicting preferences no longer a problem? Personality-based dynamic clustering for group recommender systemsExpert Systems with Applications10.1016/j.eswa.2024.124812255(124812)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124812
Turno FJackiva I(2023)Graph-Based Approach for Personalized Travel RecommendationsTransport and Telecommunication Journal10.2478/ttj-2023-003324:4(423-433)Online publication date: 17-Nov-2023
https://doi.org/10.2478/ttj-2023-0033
Liang BMeng XZhang Y(2023)Exploring Time-aware Multi-pattern Group Venue Recommendation in LBSNsACM Transactions on Information Systems10.1145/356428041:3(1-31)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1145/3564280
Bahari Sojahrood ZTaleai MCheng H(2023)Hybrid POI group recommender system based on group type in LBSNExpert Systems with Applications10.1016/j.eswa.2023.119681219(119681)Online publication date: Jun-2023
https://doi.org/10.1016/j.eswa.2023.119681
Teoman HKaragoz PHong JBures MPark JCerny T(2022)Group oriented trust-aware location recommendation for location-based social networksProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507154(1779-1788)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3477314.3507154
Bahari Sojahrood ZTaleai M(2021)A POI group recommendation method in location-based social networks based on user influenceExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.114593171:COnline publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1016/j.eswa.2021.114593
Liu CLiu JXu SWang JLiu CChen TJiang T(2020)A Spatiotemporal Dilated Convolutional Generative Network for Point-Of-Interest RecommendationISPRS International Journal of Geo-Information10.3390/ijgi90201139:2(113)Online publication date: 19-Feb-2020
https://doi.org/10.3390/ijgi9020113
Zhou FWu HTrajcevski GKhokhar AZhang K(2020)Semi-supervised Trajectory Understanding with POI Attention for End-to-End Trip RecommendationACM Transactions on Spatial Algorithms and Systems10.1145/33788906:2(1-25)Online publication date: 7-Feb-2020
https://dl.acm.org/doi/10.1145/3378890
Zhao XZhang ZBi XSun Y(2020)A new point-of-interest group recommendation method in location-based social networksNeural Computing and Applications10.1007/s00521-020-04979-435:18(12945-12956)Online publication date: 25-May-2020
https://doi.org/10.1007/s00521-020-04979-4
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