Comparative Study on Modern Approaches of Recommender System

  • Authors

    • A. Bhanu Prasad
    • Dr. N. Sambasiva Rao
    • K. Subba Rao
    • B Lakshmi
    2018-09-25
    https://doi.org/10.14419/ijet.v7i4.6.20237
  • 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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      [2] https://www.reliablesoft.net/top-10-search-engines-in-the-world

      [3] Gediminas Adomavicius and Alexander Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensionsâ€, IEEETKDE: IEEE Transactions on Knowledge and Data Engineering, 17, 2005.

      [4] Meenakshi Sharma, Sandeep Mann, “A Survey of Recommender Systems: Approaches and Limitationsâ€, International Journal of Innovations in Engineering and Technology, Special-Issue ICAECE-2013.

      [5] Aiswarya Thomas and A.K.Sujatha, “ Comparative study of recommender systemsâ€, Circuit, Power and Computing Technologies (ICCPCT), 2016 International Conference on, 18-19 March 2016.

      [6] Greg Linden, Brent Smith and Jeremy York “Amazon.com Recommendations Item-to-Item Collaborative Filteringâ€,IEEE Internet Computing, Jan-2003.

      [7] E. Peis, J. M. Morales-del Castillo, and J. A. Delgado-Lpez, “Semantic recommender systems. analysis of the state of the topic.†Seoul, South Korea: Department of Computer Science, Yonsei Univerisity, 2008.

      [8] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative ï¬ltering recommendation algorithms,†in Proceedings of the 10th international conference on World Wide Web, ser. WWW ’01. New York, NY, USA: ACM, 2001, pp. 285–295. [Online]. Available: http://doi.acm.org/10.1145/371920.372071.

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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 & Technology, 7(4.6), 60-62. https://doi.org/10.14419/ijet.v7i4.6.20237

    Received date: 2018-09-24

    Accepted date: 2018-09-24

    Published date: 2018-09-25