LARS: A location-aware recommender system

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

263 Citations (Scopus)

Abstract

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)
Title of host publicationProceedings - International Conference on Data Engineering
Pages450-461
Number of pages12
DOIs
StatePublished - 2012
Externally publishedYes
EventIEEE 28th International Conference on Data Engineering, ICDE 2012 - Arlington, VA, United States
Duration: Apr 1 2012Apr 5 2012

Other

OtherIEEE 28th International Conference on Data Engineering, ICDE 2012
CountryUnited States
CityArlington, VA
Period4/1/124/5/12

Fingerprint

Recommender systems
Personnel rating
Taxonomies
Scalability
Lenses

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Software

Cite this

Levandoski, J. J., Elsayed, M., Eldawy, A., & Mokbel, M. F. (2012). LARS: A location-aware recommender system. In Proceedings - International Conference on Data Engineering (pp. 450-461). [6228105] https://doi.org/10.1109/ICDE.2012.54

LARS : A location-aware recommender system. / Levandoski, Justin J.; Elsayed, Mohamed; Eldawy, Ahmed; Mokbel, Mohamed F.

Proceedings - International Conference on Data Engineering. 2012. p. 450-461 6228105.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Levandoski, JJ, Elsayed, M, Eldawy, A & Mokbel, MF 2012, LARS: A location-aware recommender system. in Proceedings - International Conference on Data Engineering., 6228105, pp. 450-461, IEEE 28th International Conference on Data Engineering, ICDE 2012, Arlington, VA, United States, 4/1/12. https://doi.org/10.1109/ICDE.2012.54
Levandoski JJ, Elsayed M, Eldawy A, Mokbel MF. LARS: A location-aware recommender system. In Proceedings - International Conference on Data Engineering. 2012. p. 450-461. 6228105 https://doi.org/10.1109/ICDE.2012.54
Levandoski, Justin J. ; Elsayed, Mohamed ; Eldawy, Ahmed ; Mokbel, Mohamed F. / LARS : A location-aware recommender system. Proceedings - International Conference on Data Engineering. 2012. pp. 450-461
@inproceedings{1068122292bc443a8e4706b325a0d12d,
title = "LARS: A location-aware recommender system",
abstract = "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.",
author = "Levandoski, {Justin J.} and Mohamed Elsayed and Ahmed Eldawy and Mokbel, {Mohamed F.}",
year = "2012",
doi = "10.1109/ICDE.2012.54",
language = "English (US)",
pages = "450--461",
booktitle = "Proceedings - International Conference on Data Engineering",

}

TY - GEN

T1 - LARS

T2 - A location-aware recommender system

AU - Levandoski, Justin J.

AU - Elsayed, Mohamed

AU - Eldawy, Ahmed

AU - Mokbel, Mohamed F.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84864272135&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864272135&partnerID=8YFLogxK

U2 - 10.1109/ICDE.2012.54

DO - 10.1109/ICDE.2012.54

M3 - Conference contribution

AN - SCOPUS:84864272135

SP - 450

EP - 461

BT - Proceedings - International Conference on Data Engineering

ER -