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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Proceedings ArticleDOI
Contextualized Point-of-Interest Recommendation
TL;DR: This paper proposes a new framework for POI recommendation, which explicitly utilizes similarity with contextual information, and outperforms all the state-of-the-art methods.
Proceedings ArticleDOI
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
TL;DR: Zhang et al. as mentioned in this paper proposed a latent probabilistic generative model called LSARS to mimic the decision-making process of users' check-in activities both in home-Town and out-of-Town scenarios by adapting to user interest drift and crowd sentiments, which can learn location-aware and sentiment-aware individual interests from the contents of spatial items and user reviews.
Proceedings ArticleDOI
Point-of-Interest Demand Modeling with Human Mobility Patterns
TL;DR: A latent factor model is developed that integrates human mobility data, POI profiles, and demographic data to robustly model the POI demand of urban regions in a holistic way and is effective for identifying POI demands for different regions.
Journal ArticleDOI
NEXT: A Neural Network Framework for Next POI Recommendation
TL;DR: In this paper, a neural network framework is proposed to learn the hidden intent regarding user's next move, by incorporating meta-data information and two kinds of temporal contexts (i.e., time interval and visit time).
Book ChapterDOI
LGLMF: Local Geographical Based Logistic Matrix Factorization Model for POI Recommendation
Hossein A. Rahmani,Mohammad Aliannejadi,Sajad Ahmadian,Mitra Baratchi,Mohsen Afsharchi,Fabio Crestani +5 more
TL;DR: An effective geographical model is proposed by considering the user's main region of activity and the relevance of each location within that region and is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation.
References
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