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Title: :  LARS*: An Efficient and Scalable Location-Aware Recommender System
PaperId: :  4374
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 3 Issue 2 2017
DUI:    16.0415/IJARIIE-4374
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Dongre Deepak MahapatravP.D.E.A.'s COE Manjari (Bk)
Nagargoje Nilesh RameshP.D.E.A.'s COE Manjari (Bk)
Karale Mahendra AnilP.D.E.A.'s COE Manjari (Bk)

Abstract

Computer Engineering
Recommender system, Spatial location, Social.
The problem of hyper-local place ranking. Given a user location and query string (e.g., “Indian restaurant"), hyper-local ranking provides a list of top-k points of interest influenced by previously logged directional queries (e.g., map direction searches from point A to point B).This paper proposes LARS*, a location-aware recommender system that uses their location-based ratings to show recommendations. Traditional recommender systems do not have spatial properties of users nor items; LARS*, next, 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 Movie Lens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches. Our proposed location-aware recommender system, tackles a problem untouched by traditional recommender systems by dealing with three types of location-based ratings: spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* employs user partitioning and travel penalty techniques to support spatial ratings and spatial items, respectively.

Citations

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IJARIIE Dongre Deepak Mahapatrav, Nagargoje Nilesh Ramesh, and Karale Mahendra Anil. "LARS*: An Efficient and Scalable Location-Aware Recommender System" International Journal Of Advance Research And Innovative Ideas In Education Volume 3 Issue 2 2017 Page 2034-2038
MLA Dongre Deepak Mahapatrav, Nagargoje Nilesh Ramesh, and Karale Mahendra Anil. "LARS*: An Efficient and Scalable Location-Aware Recommender System." International Journal Of Advance Research And Innovative Ideas In Education 3.2(2017) : 2034-2038.
APA Dongre Deepak Mahapatrav, Nagargoje Nilesh Ramesh, & Karale Mahendra Anil. (2017). LARS*: An Efficient and Scalable Location-Aware Recommender System. International Journal Of Advance Research And Innovative Ideas In Education, 3(2), 2034-2038.
Chicago Dongre Deepak Mahapatrav, Nagargoje Nilesh Ramesh, and Karale Mahendra Anil. "LARS*: An Efficient and Scalable Location-Aware Recommender System." International Journal Of Advance Research And Innovative Ideas In Education 3, no. 2 (2017) : 2034-2038.
Oxford Dongre Deepak Mahapatrav, Nagargoje Nilesh Ramesh, and Karale Mahendra Anil. 'LARS*: An Efficient and Scalable Location-Aware Recommender System', International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 2, 2017, p. 2034-2038. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/LARS___An_Efficient_and_Scalable_Location_Aware_Recommender_System_ijariie4374.pdf (Accessed : 21 October 2022).
Harvard Dongre Deepak Mahapatrav, Nagargoje Nilesh Ramesh, and Karale Mahendra Anil. (2017) 'LARS*: An Efficient and Scalable Location-Aware Recommender System', International Journal Of Advance Research And Innovative Ideas In Education, 3(2), pp. 2034-2038IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/LARS___An_Efficient_and_Scalable_Location_Aware_Recommender_System_ijariie4374.pdf (Accessed : 21 October 2022)
IEEE Dongre Deepak Mahapatrav, Nagargoje Nilesh Ramesh, and Karale Mahendra Anil, "LARS*: An Efficient and Scalable Location-Aware Recommender System," International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 2, pp. 2034-2038, Mar-App 2017. [Online]. Available: https://ijariie.com/AdminUploadPdf/LARS___An_Efficient_and_Scalable_Location_Aware_Recommender_System_ijariie4374.pdf [Accessed : 21 October 2022].
Turabian Dongre Deepak Mahapatrav, Nagargoje Nilesh Ramesh, and Karale Mahendra Anil. "LARS*: An Efficient and Scalable Location-Aware Recommender System." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 3 number 2 (21 October 2022).
Vancouver Dongre Deepak Mahapatrav, Nagargoje Nilesh Ramesh, and Karale Mahendra Anil. LARS*: An Efficient and Scalable Location-Aware Recommender System. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2017 [Cited : 21 October 2022]; 3(2) : 2034-2038. Available from: https://ijariie.com/AdminUploadPdf/LARS___An_Efficient_and_Scalable_Location_Aware_Recommender_System_ijariie4374.pdf
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