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A Case Study on Various Recommendation Systems

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Athulya R. Krishnan, Remya R.

Athulya R Krishnan and Remya R.. Article: A Case Study on Various Recommendation Systems. International Journal of Computer Applications 133(15):5-8, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Athulya R. Krishnan and Remya R.},
	title = {Article: A Case Study on Various Recommendation Systems},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {15},
	pages = {5-8},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


The goal of a recommender system is to generate relevant recommendations for users. It is an information filtering technique that assists users by filtering the redundant and unwanted data from a data chunk and delivers relevant information to the users. An information system is known as recommendation engine when the delivered information comes in the form of suggestions. Since different users have different interests, the information filtering system must be personalized to accommodate the individual users interests. This requires gathering of feedbacks from the user in order to make a user profile of his preferences. Recommender systems have become extremely common in the recent years, and are applied in a variety of fields. Usually recommender systems are based on the keyword search which allows the efficient scanning of very large document collections. Recommender systems typically produce a list of recommendations through collaborative or content-based filtering techniques.Different recommendation systems are available for different scenarios. In this paper, a detailed review of various recommendation systems is presented.


  1. Greg Linden, Brent Smith, and Jeremy York,” Recommendations :Item-to-Item Collaborative Filtering IEEE Computer Society, 2008.
  2. B. Rhodes and T.Starner , ”Remembrance Agent: A continuously running automated information retrieval system, in Proc. 1st Int. Conf.Pract. Applicat. Intell. Agents Multi Agent Technol., London, U.K.,1996, pp. 487495.
  3. B. J. Rhodes and P. Maes, Just-in-time information retrieval agents,IBM Syst. J., vol. 39, no. 3.4, pp. 685704, 2000.
  4. J. Budzik and K. J. Hammond, User interactions with everyday applications as context for just-in-time information access, in Proc. 5th Int. Conf. Intell. User Interfaces (IUI00), 2000, pp. 4451.
  5. A. S. M. Arif, J. T. Du, and I. Lee, Towards a model of collaborative information retrieval in tourism, in Proc. 4th Inf. Interact. Context Symp., 2012, pp. 258261.
  6. A. S. M. Arif, J. T. Du, and I. Lee, Examining collaborative query reformulation: A case of travel information searching, in Proc. 37th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2014, pp.875878.
  7. D. Traum, P. Aggarwal, R. Artstein, S. Foutz, J. Gerten, A. Katsamanis,A. Leuski, D. Noren, and W. Swartout, Ada and Grace: Direct interaction with museum visitors, in Proc. 12th Int. Conf. Intell. Virtual Agents, 2012, pp. 245251.
  8. David Traum, William Swartout, Ada and Grace: Toward Realistic and Engaging Virtual Museum Guides, Springer- Verlag Berlin Heidelberg 2010.
  9. S. Dumais, E. Cutrell, R. Sarin, and E. Horvitz, Implicit queries (IQ) for contextualized search, in Proc. 27th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval, 2004, pp. 594594.
  10. M. Czerwinski, S. Dumais, G. Robertson, S. Dziadosz, S. Tiernan, and M. Van Dantzich, Visualizing implicit queries for information management and retrieval, in Proc. SIGCHI Conf. Human Factors Comput. Syst. (CHI), 1999, pp. 560567.


Collaboration, Content based, Information filtering