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Conference Paper: Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks

TitleFused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks
Authors
Issue Date2012
Citation
Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012, 2012, p. 178-184 How to Cite?
AbstractRecently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users' preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user's check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.
Persistent Identifierhttp://hdl.handle.net/10722/349945

 

DC FieldValueLanguage
dc.contributor.authorCheng, Chen-
dc.contributor.authorYang, Haiqin-
dc.contributor.authorKing, Irwin-
dc.contributor.authorLyu, Michael R.-
dc.date.accessioned2024-10-17T07:02:02Z-
dc.date.available2024-10-17T07:02:02Z-
dc.date.issued2012-
dc.identifier.citationProceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012, 2012, p. 178-184-
dc.identifier.urihttp://hdl.handle.net/10722/349945-
dc.description.abstractRecently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users' preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user's check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.-
dc.languageeng-
dc.relation.ispartofProceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012-
dc.titleFused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-85167399956-
dc.identifier.spage178-
dc.identifier.epage184-

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