LARS: An Efficient and Scalable Location-Aware Recommender System

• Published in: IEEE transactions on knowledge and data engineering 2014-06, Vol.26 (6), p.1384-1399
• Main Authors: Sarwat, Mohamed, Levandoski, Justin J., Eldawy, Ahmed, Mokbel, Mohamed F.
• Format: Article
• Language: English
• Subjects: Collaboration; Data structures; Database systems; Maintenance engineering; Motion pictures; Recommender systems; Scalability
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2013.29

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 together, 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 MovieLens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.