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

• 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.
• 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.
• Bilgic and R. J. Mooney,"Explaining recommendations: Satisfaction vs. promotion", In Beyond Personalization Workshop, IUI, January, 2005.
• R. Agrawal and T. Imielinski ,"A. Swami: Mining Association Rules Between Sets of Items in Large Databases", SIGMOD Conference, pages 207-216, June, 1993.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• P. Resnick, K. Kuwabara, R. Zeckhauser, and E. Friedman,"Reputation Systems", Communications of the ACM ,Vol 43,No. 12,pages 45-48,2000.
• 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.
• Hao Ma, Irwin King and Michael R. Lyu," Mining Web Graphs for Recommendations",Vol 24,No. 6,pages 1051-1064, June,2012.