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A comparative study of location-based recommendation systems

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 the availability 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, and trajectory-based information help in connecting the gap between the online social networking services and the physical world. In this article, we present a systematic review of the scientific literature of LBRS and summarize the efforts and contributions proposed in the literature. We have performed a qualitative comparison of the existing techniques used in the area of LBRS. We present the basic filtration techniques used in LBRS followed by a discussion on the services and the location features the LBRS utilizes to perform the recommendations. The classification of criteria for recommendations and evaluation metrics are also presented. We have critically investigated the techniques proposed in the literature for LBRS and extracted the challenges and promising research topics for future work.

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

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