LARS*: An Efficient and Scalable Location-Aware Recommender System

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.

  • Project Category : IEEE Projects
  • Project Year : 2014-2015
  • Department
  • B.E(Computer Science) , B.E(Electronics and Communication), B.E(Information Technology), B.SC(CS), B.SC(IT), BCA, M.E(Computer Science), M.E(SOFTWATE ENGG), M.SC(CS&M), M.SC(CS), M.SC(IT&M), M.SC(IT), M.SC(SOFTWARE ENGG), MCA,
  • Domain
  • Data Mining,
  • Technology
  • .Net,
  • Avilable city
  • Chennai, Pondicherry,


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