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

@article{Sarwat2014LARSAE,
  title={LARS*: An Efficient and Scalable Location-Aware Recommender System},
  author={Mohamed Sarwat and Justin J. Levandoski and Ahmed Eldawy and Mohamed F. Mokbel},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2014},
  volume={26},
  pages={1384-1399}
}
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… CONTINUE READING
Highly Cited
This paper has 102 citations. REVIEW CITATIONS

14 Figures & Tables

Topics

Statistics

02040201320142015201620172018
Citations per Year

103 Citations

Semantic Scholar estimates that this publication has 103 citations based on the available data.

See our FAQ for additional information.