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A comparative study of location-based recommendationsystems

Published online by Cambridge University Press:  16 January 2017

Faisal Rehman ,
Osman Khalid  and
Sajjad Ahmad Madani
Faisal Rehman
Affiliation:
Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan e-mail: frehman@ciit.net.pk, osman@ciit.net.pk, madani@ciit.net.pk
Osman Khalid
Affiliation:
Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan e-mail: frehman@ciit.net.pk, osman@ciit.net.pk, madani@ciit.net.pk
Sajjad Ahmad Madani
Affiliation:
Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad 22060, Pakistan e-mail: frehman@ciit.net.pk, osman@ciit.net.pk, madani@ciit.net.pk
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Abstract

Recent advancements in location-based recommendation system (LBRS) and theavailability of online applications, such as Twitter, Instagram, Foursquare,Path, and Facebook have introduced new research challenges in the area of LBRS.Use of content, such as geo-tagged media, point location-based, andtrajectory-based information help in connecting the gap between the onlinesocial networking services and the physical world. In this article, we present asystematic review of the scientific literature of LBRS and summarize the effortsand contributions proposed in the literature. We have performed a qualitativecomparison of the existing techniques used in the area of LBRS. We present thebasic filtration techniques used in LBRS followed by a discussion on theservices and the location features the LBRS utilizes to perform therecommendations. The classification of criteria for recommendations andevaluation metrics are also presented. We have critically investigated thetechniques proposed in the literature for LBRS and extracted the challenges andpromising research topics for future work.

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Survey Article
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The Knowledge Engineering Review , Volume 32 , 2017 , e7
Copyright
© Cambridge University Press, 2017 

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