LARS: A location-aware recommender system

Justin J. Levandoski, Mohamed Sarwat, Ahmed Eldawy, Mohamed F. Mokbel

Research output: Contribution to journalConference articlepeer-review

290 Scopus citations


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.

Original languageEnglish (US)
Article number6228105
Pages (from-to)450-461
Number of pages12
JournalProceedings - International Conference on Data Engineering
StatePublished - Jul 30 2012
EventIEEE 28th International Conference on Data Engineering, ICDE 2012 - Arlington, VA, United States
Duration: Apr 1 2012Apr 5 2012

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