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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Book ChapterDOI
Geographical and Overlapping Community Modeling Based on Business Circles for POI Recommendation
TL;DR: A new geographical model with social influence and user preference which is more suitable for the modern consumption pattern in an urban city in POI recommendation is proposed and the experimental results demonstrate that the model outperforms other existing algorithms.
Journal ArticleDOI
Exploiting organizer influence and geographical preference for new event recommendation
TL;DR: This paper combines user interest, organizer influence, and geographical preference to present a unified model for new event recommendation and conducts a performance evaluation using real‐world data collected from DoubanEvent to demonstrate the effectiveness of the proposed model.
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Magnitude Bounded Matrix Factorisation for Recommender Systems
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Why people go to unfamiliar areas?: analysis of mobility pattern based on users' familiarity
Jungkyu Han,Hayato Yamana +1 more
TL;DR: This paper analyzes familiarity's effect on mobility patterns by using over 1.4 million check-ins gathered from Foursquare to indicate that there is a skewness of the visit time and visited venue distribution in unfamiliar areas.
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