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Fused matrix factorization with geographical and social influence in location-based social networks

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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 significantly

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TICRec: A Probabilistic Framework to Utilize Temporal Influence Correlations for Time-Aware Location Recommendations

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Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence

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Low-Rank Matrix Factorization under General Mixture Noise Distributions

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References
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Proceedings Article

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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.
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