Book Recommendation System Based on Combine Features of Content Based Filtering, Collaborative Filtering and Association Rule Mining Ashish Fatarphekar
DOI:
https://doi.org/10.53555/nnmce.v2i3.349Keywords:
Association rule, Collaborative filtering,, Content based filtering, Recommendation systemAbstract
Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer’s interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.
References
FOLTZ, P. W. AND DUMAIS, S. T. 1992. Personalized information delivery: an analysis of information filtering methods. Comm. ACM 35(12), pp. 51–60.
SHARDANAND, U. AND MAES, P. 1995. Social information filtering: algorithms for automating “word of mouth”. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Resnick, P.,and Hal, R. V., 1997. Recommender Systems,Communications of the ACM, 40, 3, pp. 56-58.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl. J., 1994. GroupLens: An Open Architecture for Collaborative Filtering of Netnews, Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, Chapel Hill, NC, pp.175-186.
Baraglia R., Silvestri F.: Dynamic Personalization of Web Sites Without User Intervention. Comm. ACM, Vol. 50, 2 (Feb., 2007): pp. 63-67.
Eirinaki, M. and Vazirgiannis, M.: Web Mining for Web Personalization. ACM Trans. On Internet Technology, Vol. 3, 1 (Feb., 2003): pp. 1-27.
Thede, L.,Marshall, V.A.,Rick W.:An Economic Answer to Unsolicited Communication. EC’04. (2004).
SARWAR, B., KARYPIS, G., KONSTAN, J., AND REIDL, J. 2001. Item-based collaborative filtering recommendation algorithms. In
Proceedings of the 10th International Conference on World Wide Web (WWW’01). ACM, New York, NY, pp.285–295.
J. Han, M. Kamber, Data Mining: Concepts and Techniques, The Morgan Kaufmann Series, 2001.
Agrawal, R., Imielinski, T., Swami, A. N. "Mining association rules between sets of items in large databases". In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207-216, 1993.
Luo Zhenghua, ”Realization Of Individualized Recommendation System On Books Sale” IEEE 2012 International Conference on Management of e-Commerce and e-Government. pp.10-13.
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