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2017 Volume 32
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RESEARCH ARTICLE   Open Access    

A comparative study of location-based recommendation systems

  • Department of Computer Science

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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.
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  • Cite this article

    Faisal Rehman, Osman Khalid, Sajjad Ahmad Madani. 2017. A comparative study of location-based recommendation systems. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000308
    Faisal Rehman, Osman Khalid, Sajjad Ahmad Madani. 2017. A comparative study of location-based recommendation systems. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000308
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RESEARCH ARTICLE   Open Access    

A comparative study of location-based recommendation systems

  • Department of Computer Science

  • Published online: 16 January 2017
The Knowledge Engineering Review  32,  2017, 32(1)  |  Cite this article

Abstract: 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.

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    • The authors would like to acknowledge Dr. Osman Khalid and Dr. Sajjad Ahmad Madani for providing their valuable suggestions, feedback, and precious time.

    • http://geo-twitter.appspot.com/

    • https://www.flickr.com/

    • http://www.panoramio.com/

    • http://www.bikely.com/

    • http://research.microsoft.com/en-us/projects/geolife/

    • www.sportsdo.com.br/

    • http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/

    • MovieLens: https://movielens.org

    • Foursquare: http://foursquare.com

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    Faisal Rehman, Osman Khalid, Sajjad Ahmad Madani. 2017. A comparative study of location-based recommendation systems. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000308
    Faisal Rehman, Osman Khalid, Sajjad Ahmad Madani. 2017. A comparative study of location-based recommendation systems. The Knowledge Engineering Review 32(1), doi: 10.1017/S0269888916000308
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