Open AccessProceedings Article
Fused matrix factorization with geographical and social influence in location-based social networks
Chen Cheng,Haiqin Yang,Irwin King,Michael R. Lyu +3 more
- Vol. 26, Iss: 1, pp 17-23
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TLDR
This paper is the first to fuse MF with geographical and social influence for POI recommendation in LBSNs via modeling the probability of a user's check-in on a location as a Multicenter Gaussian Model (MGM) and fuse the geographical influence into a generalized matrix factorization framework.Abstract:
Recently, 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, eg, 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 Multicenter 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 significantlyread more
Citations
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Journal ArticleDOI
Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media
TL;DR: This paper proposes an Interest-Aware Location-Based Recommender system (IALBR), which combines the advantages of both LBSNs and SNs, in order to provide interest-aware location-based recommendations and evaluates the system with a large-scale real dataset.
Journal ArticleDOI
Exploring Temporal and Spatial Features for Next POI Recommendation in LBSNs
TL;DR: Wang et al. as discussed by the authors proposed a next POI recommendation model that will predict POIs to be visited by users in the next few hours according to their historical check-in data and current contextual information (such as the current time and locations of the users).
Journal ArticleDOI
Mining Check-In History for Personalized Location Naming
Defu Lian,Xing Xie +1 more
TL;DR: A novel location naming approach which can automatically provide semantic names for users given their locations and time is proposed, and SP was most effective among three components and that UP can provide personalized semantic names, and thus it was a necessity for location naming.
Journal ArticleDOI
Friend and POI recommendation based on social trust cluster in location-based social networks
TL;DR: This paper proposes algorithm to identify trust clusters and then gives a trust prediction method based on these trust clusters to recommend friends to the target user and devise a hybrid framework that integrates user preference, geographical influence, and trust relationship to improve the recommendation quality.
Proceedings ArticleDOI
R2SIGTP: a Novel Real-Time Recommendation System with Integration of Geography and Temporal Preference for Next Point-of-Interest
TL;DR: R2SIGTP is easy to use and can be used by the mobile terminal's browser to recommend the next POI to the user in real-time based on the automatically identified user location and current time and its performance is satisfactory.
References
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