Comparative Study on Modern Approaches of Recommender System

  • Authors

    • A. Bhanu Prasad
    • Dr. N. Sambasiva Rao
    • K. Subba Rao
    • B Lakshmi
    https://doi.org/10.14419/ijet.v7i4.6.20237

    Received date: September 24, 2018

    Accepted date: September 24, 2018

    Published date: September 25, 2018

  • Data mining, Recommender System, Filtering Approaches

  • Abstract

    Recommender system is a kind of tool for filtering information and items of user interest. There are large number of different approaches for filtering data and information. In this paper a comparative study is made on different modern approaches in particular. All the modern approaches along with traditional recommender systems are listed and explained with their merits and demerits. Some common challenges are also addressed in this context.

  • References

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    6. Greg Linden, Brent Smith and Jeremy York “Amazon.com Rec-ommendations Item-to-Item Collaborative Filtering”,IEEE Inter-net Computing, Jan-2003.
    7. E. Peis, J. M. Morales-del Castillo, and J. A. Delgado-Lpez, “Se-mantic recommender systems. analysis of the state of the topic.” Seoul, South Korea: Department of Computer Science, Yonsei Univerisity, 2008.
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  • How to Cite

    Bhanu Prasad, A., N. Sambasiva Rao, D., Subba Rao, K., & Lakshmi, B. (2018). Comparative Study on Modern Approaches of Recommender System. International Journal of Engineering and Technology, 7(4.6), 60-62. https://doi.org/10.14419/ijet.v7i4.6.20237