Where could we go? Recommendations for groups in location-based social networks

dc.contributorAalto Universityen
dc.contributor.authorAyala-Gomez, Fredericken_US
dc.contributor.authorDaróczy, Bálinten_US
dc.contributor.authorMathioudakis, Michaelen_US
dc.contributor.authorBenczúr, Andrásen_US
dc.contributor.authorGionis, Aristidesen_US
dc.contributor.departmentEötvös Loránd Universityen_US
dc.contributor.departmentHungarian Academy of Sciencesen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.description| openaire: EC/H2020/654024/EU//SoBigData
dc.description.abstractLocation-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.en
dc.description.versionPeer revieweden
dc.identifier.citationAyala-Gomez , F , Daróczy , B , Mathioudakis , M , Benczúr , A & Gionis , A 2017 , Where could we go? Recommendations for groups in location-based social networks . in WebSci 2017 - Proceedings of the 2017 ACM Web Science Conference . ACM , pp. 93-102 , ACM Web Science Conference , Troy , New York , United States , 25/06/2017 . https://doi.org/10.1145/3091478.3091485en
dc.identifier.otherPURE UUID: 936087ec-1475-4277-adf3-4ac3741ddda7en_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/936087ec-1475-4277-adf3-4ac3741ddda7en_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85026772454&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/26625705/wherecouldwego.pdfen_US
dc.relation.ispartofACM Web Science Conferenceen
dc.relation.ispartofseriesWebSci 2017 - Proceedings of the 2017 ACM Web Science Conferenceen
dc.subject.keywordGroup recommendationen_US
dc.subject.keywordLocation-based social networksen_US
dc.subject.keywordRecommender systemsen_US
dc.titleWhere could we go? Recommendations for groups in location-based social networksen
dc.typeConference article in proceedingsfi