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
Time-aware point-of-interest recommendation
TL;DR: This paper defines a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day, and develops a collaborative recommendation model that is able to incorporate temporal information.
Proceedings Article
Predicting the next location: a recurrent model with spatial and temporal contexts
TL;DR: RNN is extended and a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN) is proposed, which can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transitions for different geographical distances.
Proceedings ArticleDOI
GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation
TL;DR: The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon in human mobility behavior on the LBSNs into matrixfactorization improves recommendation performance.
Journal ArticleDOI
Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs
TL;DR: A STAP model is proposed that first models the spatial and temporal activity preference separately, and then uses a principle way to combine them for preference inference, and a context-aware fusion framework is put forward to combine the temporal and spatial activity preference models for preferences inference.
Proceedings Article
Where you like to go next: successive point-of-interest recommendation
TL;DR: This paper proposes a novel matrix factorization method, namely FPMC-LR, to embed the personalized Markov chains and the localized regions in the check-in sequence, and utilizes the information of localized regions to boost recommendation.
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