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Present Scenario of Recommendation System in Web

by S. Vasukipriya, T. Vijaya Kumar, S. Vinoth Sarun
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 1
Year of Publication: 2013
Authors: S. Vasukipriya, T. Vijaya Kumar, S. Vinoth Sarun
10.5120/11046-5938

S. Vasukipriya, T. Vijaya Kumar, S. Vinoth Sarun . Present Scenario of Recommendation System in Web. International Journal of Computer Applications. 66, 1 ( March 2013), 5-8. DOI=10.5120/11046-5938

@article{ 10.5120/11046-5938,
author = { S. Vasukipriya, T. Vijaya Kumar, S. Vinoth Sarun },
title = { Present Scenario of Recommendation System in Web },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 1 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number1/11046-5938/ },
doi = { 10.5120/11046-5938 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:21:10.315369+05:30
%A S. Vasukipriya
%A T. Vijaya Kumar
%A S. Vinoth Sarun
%T Present Scenario of Recommendation System in Web
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 1
%P 5-8
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper is a survey on recent work in the field of recommendation system in web mining. Internet users often spend more time in finding useful pages. Recommendation system does such a job, that it can help the user to gather more information and it increases the user's loyalty. Web mining is good in dealing with massive data and sparse data.

References
  1. Batul J. Mirza, Benjamin J. Keller and Naren Ramakrishnan," Studying Recommendation Algorithms by Graph Analysis", journal of Intelligent Information Systems, Vol. 20, Pages 131 – 160, March ,2003.
  2. Hill, W. , L. Stead, M. Rosenstein, and G. Furnas, "Recommending and evaluating choices in a virtual community of use", In Proceedings of CHI, pages 194-201, May ,1995.
  3. Bilgic and R. J. Mooney,"Explaining recommendations: Satisfaction vs. promotion", In Beyond Personalization Workshop, IUI, January, 2005.
  4. R. Agrawal and T. Imielinski ,"A. Swami: Mining Association Rules Between Sets of Items in Large Databases", SIGMOD Conference, pages 207-216, June, 1993.
  5. R. Agrawal and R. Srikant. "Fast algorithms for mining association rules", Proceedings of the 20th International Conference on Very Large Data Bases, Pages 487 - 499 Santiago, Chile, Sep,1994.
  6. Bardul M. Sarwar, George Karypis, Joseph A. Konstan, and John T. Riedl, "Item-based collaborative filtering recommendation algorithms," Proceedings of the 10th international conference on World Wide Web, Pages 285-295, Hong Kong, 2001.
  7. George Karypis, "Evaluation of item-based top-n recommendation algorithms," Proceedings of the tenth international conference on Information and knowledge management, Pages 247 - 254, 2001.
  8. Devi. M. K. K, Samy. R. T, Kumar. S. V and Venkatesh. P," Probabilistic neural network approach to alleviate sparsity and cold start problems in collaborative recommender systems", Computational Intelligence and Computing Research , pages 1-4, Dec,2010.
  9. Barrilero. M, Uribe. S, Alduan. M,Sanchez. F and Alvarez. F," In-network content based image recommendation system for Content-aware Networks", Computer Communications Workshops, pages 115-120, April,2011.
  10. G. Adomavicius, A. Tuzhilin. "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions". IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6. pages 734-742, June,2006.
  11. B. Sheth and P. Maes, "Evolving Agents for Personalized Information Filtering". Proceedings to the Ninth Conference on Artificial Intelligence for Applications, pages 345-352, March,1993.
  12. Zhang. Z, Zhou. T and Zhang Y, "Personalized Recommendation via Integrated Diffusion on User-Item- Tag Tripartite Graphs", Journal Article, Vol 389, pages 179–186, January ,2010.
  13. Z. Huang, H. Chen, and D. Zeng,"Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering", ACM Transactions on Information Systems ,Vol 22,No. 1,pages 116-142,January,2004.
  14. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl,"Evaluating Collaborative Filtering Recommender Systems",ACM Transactions on Information Systems,Vol 22,No. 1,pages 5-53, January,2004.
  15. P. Resnick, K. Kuwabara, R. Zeckhauser, and E. Friedman,"Reputation Systems", Communications of the ACM ,Vol 43,No. 12,pages 45-48,2000.
  16. Yi-hsuan Yang , Po-tun Wu , Ching-wei Lee , Kuan-hung Lin , Winston H. Hsu and Homer Chen,"ContextSeer: Context search and recommendation at query time for shared consumer photos", Proceedings of the 16th ACM international conference on Multimedia, Pages 199-208,2008.
  17. Hao Ma, Irwin King and Michael R. Lyu," Mining Web Graphs for Recommendations",Vol 24,No. 6,pages 1051-1064, June,2012.
Index Terms

Computer Science
Information Sciences

Keywords

Recommendation Systems Web Mining

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