LARS*: An efficient and scalable location-aware recommender system

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

Research output: Contribution to journalArticlepeer-review

146 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 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.

Original languageEnglish (US)
Article number6427747
Pages (from-to)1384-1399
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number6
StatePublished - Jun 2014
Externally publishedYes


  • database
  • recommender systems

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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