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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Book ChapterDOI
A Point of Interest Recommendation Approach by Fusing Geographical and Reputation Influence on Location Based Social Networks
TL;DR: A novel POI recommendation approach is proposed by fusing user preference, geographical influence and social reputation, and TFIDF is used to represent user preference that improves recommendation model by incorporating geographical distance and popularity.
Proceedings ArticleDOI
Hierarchical and Multi-Resolution Preference Modeling for Next POI Recommendation
TL;DR: Wang et al. as discussed by the authors proposed Hierarchical and Multi-Resolution Preference Modeling (HMRPM), which simultaneously models hierarchical personalized preferences at three different resolutions, i.e., long-term, short-term and transient preferences.
Book ChapterDOI
RCFC: A Region-Based POI Recommendation Model with Collaborative Filtering and User Context
Jun Zeng,Haoran Tang,Xin He +2 more
TL;DR: Zhang et al. as mentioned in this paper proposed a region-based collaborative filtering to alleviate the data sparseness by clustering locations into regions and model the impact of two kinds of user contexts like geographical distance and POI category to make POI recommendation more reasonable.
Journal ArticleDOI
Modeling user information needs on mobile devices: from recommendation to conversation
TL;DR: This thesis investigates three methods of user modeling, namely, content-based, collaborative, and hybrid, focusing on personalization and context-awareness, and introduces and investigates a new task on mobile search, that is, unified mobile search.
Dissertation
Spatiotemporal user and place modelling on the geo-social web
TL;DR: This thesis investigates the different dimensions of data collected on LBSNs and proposes a user and place modelling framework and a novel approach for the construction of different views of personal user profiles that reflect their interest in geographic places, and how they interact with geographic places.
References
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Proceedings Article
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