Sindbad: a location-based social networking system
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Location-based and preference-aware recommendation using sparse geo-social networking data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
The anatomy of Sindbad: a location-aware social networking system
Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Location context aware collective filtering algorithm
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
LCARS: a location-content-aware recommender system
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Location recommendation for out-of-town users in location-based social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
LearNext: learning to predict tourists movements
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Landmark-based user location inference in social media
Proceedings of the first ACM conference on Online social networks
iGSLR: personalized geo-social location recommendation: a kernel density estimation approach
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Location recommendation in location-based social networks using user check-in data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Mobility and social networking: a data management perspective
Proceedings of the VLDB Endowment
Analysis of a context-aware recommender system model for smart urban environment
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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This paper proposes LARS, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items, LARS, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or in concert, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the Movie Lens movie recommendation system reveals that LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.