Location Recommendation for Location-based Social Networks
Mao Ye Peifeng Yin Wang-Chien Lee Department of Computer Science and Engineering The Pennsylvania State University University Park, PA 16802, USA {mxy177,pzy102,wlee}@cse.psu.edu 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. networking services allow users to connect with friends, explore places (e.g., restaurants, stores, cinema theaters, etc), share their locations, and upload photos, video, and blogs. As city and neighborhood exploration is one of the main themes in many location-based social networking services, it is highly desirable for such services to provide location recommendations to their users. Moreover, as the users and locations in location-based social networking