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 Article
Learning Recency and Inferring Associations in Location Based Social Network for Emotion Induced Point-of-Interest Recommendation.
Logesh Ravi,V. Subramaniyaswamy +1 more
TL;DR: An effective recommendation model for the location recommendation through exploiting the emotion of the user from online social media is presented and the improved efficiency and accuracy is proved through validation by standard evaluation metrics.
Proceedings Article
On information coverage for location category based point-of-interest recommendation
TL;DR: A new POI recommendation problem, namely top-K location category basedPOI recommendation, is formulated by introducing information coverage to encode the location categories of POIs in a city by developing a greedy algorithm and further optimization to solve this challenging problem.
Book ChapterDOI
Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation
TL;DR: Zhang et al. as discussed by the authors proposed a spatio-temporal activity-centers algorithm to model users' behavior more accurately by incorporating contextual information such as geographical and temporal influences to improve POI recommendation by addressing the data sparsity problem.
Journal ArticleDOI
Integrating spatial and temporal contexts into a factorization model for POI recommendation
TL;DR: The proposed Feature-Space Separated Factorization Model (FSS-FM) represents the POI feature spaces as separate slices, each of which represents a type of feature, and the capacity of hybrid optimization in improving POI recommendation is demonstrated.
Journal ArticleDOI
Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations
TL;DR: This paper makes full use of the mobile users’ location sensitive characteristics to carry out rating prediction andMine the relevance between user's ratings and user-item geographical location distances, called as user-user geographical connection and conducts a series of experiments on a real social rating network dataset Yelp.
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