Proceedings ArticleDOI
Location recommendation for location-based social networks
Mao Ye,Peifeng Yin,Wang-Chien Lee +2 more
- pp 458-461
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TLDR
A friend-based collaborative filtering approach for location recommendation based on collaborative ratings of places made by social friends is developed, and a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset is proposed.Abstract:
In this paper, we study the research issues in realizing location recommendation services for large-scale location-based social networks, by exploiting the social and geographical characteristics of users and locations/places. Through our analysis on a dataset collected from Foursquare, a popular location-based social networking system, we observe that there exists strong social and geospatial ties among users and their favorite locations/places in the system. Accordingly, we develop a friend-based collaborative filtering (FCF) approach for location recommendation based on collaborative ratings of places made by social friends. Moreover, we propose a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset. Finally, the evaluation results show that the proposed family of FCF techniques holds comparable recommendation effectiveness against the state-of-the-art recommendation algorithms, while incurring significantly lower computational overhead. Meanwhile, the GM-FCF provides additional flexibility in tradeoff between recommendation effectiveness and computational overhead.read more
Citations
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Proceedings ArticleDOI
Exploiting geographical influence for collaborative point-of-interest recommendation
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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.
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Location-based and preference-aware recommendation using sparse geo-social networking data
TL;DR: A location-based and preference-aware recommender system that offers a particular user a set of venues within a geospatial range with the consideration of both: user preferences and social opinions, which are automatically learned from her location history.
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Fused matrix factorization with geographical and social influence in location-based social networks
TL;DR: 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.
Journal ArticleDOI
A new user similarity model to improve the accuracy of collaborative filtering
TL;DR: A new user similarity model is presented to improve the recommendation performance when only few ratings are available to calculate the similarities for each user, which not only considers the local context information of user ratings, but also the global preference of user behavior.
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