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
A Point-of-Interest Recommender System Based on Factorization Machines
Long-fei Tu,Dong Wang +1 more
TL;DR: This paper first gives a precise analysis of the self-similar and other-similar characteristics in users' behaviors, and accordingly proposes an improved factorization machines (FM) model with social and temporal regularization terms which exploits social, temporal and geographical information for POI recommendation based on factorization Machines.
Proceedings ArticleDOI
PRPOIR: Exploiting the Region-Level Interest for POI Recommendation
TL;DR: Zhang et al. as discussed by the authors proposed a hybrid model called POI-Region POI Recommendation (PRPOIR), which contains two modules: Interest Module and Context Module, inspired by the success of the Logistic Matrix Factorization (LMF) to model implicit feedback, they apply it to model users' POIlevel interest and region-level interest respectively.
Un Modèle de Factorisation de Poisson pour la Recommandation de Points d'Intérêt
TL;DR: Dans ce papier nous presentons un modele de recommandation basee sur the factorisation de Poisson qui offre une solution efficace a ces contraintes a sparsite tres elevee des donnees du LBSN Foursquare.
Dissertation
Exploring behavioral data in online social media with focus on user connectivity and mobility
TL;DR: This thesis analyzes users’ social connectivity behaviors from a new angle and study a problem of mining non-homophily social ties, aiming at discovering interesting but unexpected group-level social ties that do not follow the homophily phenomenon.
Proceedings ArticleDOI
User Location Prediction by Diffusion-Type Estimation Using Location-Based SNS Check-in Data
TL;DR: This paper proposes a method to predict users' future locations by utilizing the repeating nature of the check-in data and similar users' check- in data and results show that the method outperformed the existing prediction methods.
References
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Proceedings Article
Probabilistic Matrix Factorization
Andriy Mnih,Ruslan Salakhutdinov +1 more
TL;DR: The Probabilistic Matrix Factorization (PMF) model is presented, which scales linearly with the number of observations and performs well on the large, sparse, and very imbalanced Netflix dataset and is extended to include an adaptive prior on the model parameters.
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Friendship and mobility: user movement in location-based social networks
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.
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Mining interesting locations and travel sequences from GPS trajectories
TL;DR: This work first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG), and proposes a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location.
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Collaborative filtering with temporal dynamics
TL;DR: Two leading collaborative filtering recommendation approaches are revamp and a more sensitive approach is required, which can make better distinctions between transient effects and long term patterns.
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Recommender systems with social regularization
TL;DR: This paper proposes a matrix factorization framework with social regularization, which can be easily extended to incorporate other contextual information, like social tags, etc, and demonstrates that the approaches outperform other state-of-the-art methods.