Comparative Study on Modern Approaches of Recommender System

 
 
 
  • Abstract
  • Keywords
  • References
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  • 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.

     


  • Keywords


    Data mining, Recommender System, Filtering Approaches

  • References


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

      [5] Aiswarya Thomas and A.K.Sujatha, “ Comparative study of recommender systems”, 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 Filtering”,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 filtering 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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Article ID: 20237
 
DOI: 10.14419/ijet.v7i4.6.20237




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