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
Exploiting Spatial and Temporal for Point of Interest Recommendation
TL;DR: This work designs a probabilistic method to initially detect users’ spatial orientation by analyzing visibility weights of POIs which are visited by them, and combines the spatial and temporal influences to construct a unified framework for making the POI recommendation.
Journal ArticleDOI
Providing recommendations on location-based social networks
Pavlos Kosmides,Konstantinos Demestichas,Evgenia Adamopoulou,Chara Remoundou,Ioannis Loumiotis,Michael E. Theologou,Miltiades E. Anagnostou +6 more
TL;DR: A novel method for predicting a user’s location based on machine learning techniques is presented and following the incremental trend towards data accumulation in social networks, a clustering based prediction method is introduced in order to enhance the recommender system.
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Effective rating prediction based on selective contextual information
TL;DR: Experimental results, with respect to rating prediction quality and recommendation performance on both public available and large created contextual datasets, show that the proposal outperforms the existing recommender systems especially on the created datasets.
Journal ArticleDOI
A coarse-to-fine user preferences prediction method for point-of-interest recommendation
TL;DR: A two-stage coarse-to-fine POI recommendation algorithm based on tensor factorization and weighted distance kernel density estimation (KDE) that outperforms the state-of-the-art methods and produces betterPOI recommendation.
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Review Selection Using Micro-Reviews
TL;DR: This paper uses coverage of micro-reviews as an objective for selecting a set of reviews that cover efficiently the salient aspects of an entity, and formulate this objective as a combinatorial optimization problem, and shows how to derive an optimal solution using Integer Linear Programming.
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