| 參考文獻 |
[1] J. Ben Schafer, Joseph A. Konstan, and John Riedl, “E-Commerce recommendation applications”, Data mining and knowledge discovery, 5, pp 115-153, 2001.
[2] Ayhan Demiriz, “Enhancing product recommender systems on sparse binary data”, Under journal review, Feb.2002.
[3] Marko Balabanovic and Yoav Shoham, “ Fab: Content-based, collaborative recommender”, Communications of the ACM, 40(3):pp 66–72, March 1997.
[4] Cyrus Shahbi and Yi-shin Chen “An adaptive recommendation system without explicit acquisition of user relevance feedback”, Kluwer academic Publishers, 2002.
[5] Krulwich, B., and Burkey, C., “Learning user information interests through extraction of semantically significant phrases”, AAAI Spring Symposium on Machine Learning in Information Access, March 1996.
[6] Lang, K. “Newsweeder: Learning to filter netnews”, The 12th International Conference on Machine Learning, 1995.
[7] Harman, D., “Over view of the 3rd Text Retrieval Conference”, The 3rd Text Retrieval Conference, Nov 1994.
[8] Zhaoxia Wang, “Collaborative filtering using error-tolerant fascicles”, Simon Fraser University, March 2001.
[9] Paul Resnick, Neophytos Iacovou, Mitesh Sushak, Peter Bergstrom, John Riedl, “GroupLens: An open architecture for collaborative filtering of netnews”, CSCW 1994 conference, Oct. 1994.
[10] Hill, W., Stead, L., Rosenstein, M., and Furnas, G., “Recommending and evaluating choices in a virtual community of us”, Conference on Human Factors in Computing Systems, May 1995.
[11] Shardanand, U., and Maes, P., “Social information filtering: Algorithms for automating “word of mouth”, Conference on Human Factors in Computing Systems, May 1995.
[12] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, “Item-based Collaborative Filtering Recommendation Algorithms”, the 10th International World Wide Web Conference, May 2001.
[13] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, “Analysis of Recommendation Algorithms for E-Commerce”, EC’00 ACM, 2000.
[14] Lyle H. Ungar and Dean P. Foster, “Clustering methods for collaborative filtering”, Fifteenth National Conference on Artificial Intelligence, July 1998.
[15] John S. Breese, David Heckerman and Carl Kadie, “Empirical analysis of predictive algorithms for collaborative filtering”, Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, pp 43–52, July 1998.
[16] Jiawei Han, Micheline Kamber, “Data mining: Concepts and Techniques”, Morgan Kaufmann, 2000
[17] J. MacQueen, “Some methods for classification and analysis of multivariate observations”, 5th Berkeley Symp. Math. Statist, Prob.,1: pp281-297, 1967
[18] L.Kaufman and P.J Rousseeuw, “Finding group in data: an introduction to cluster analysis, New York: John Wiley & Sons, 1990.
[19] R.Ng and J. Han. “Efficient and effective clustering method for spatial data mining”, 1994 Int. Conf. VLDB, pp 144-155, Sept. 1994.
[20] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu., “A density-based algorithm for discovering clusters in large spatial databases”, 1996 Int. Conf. KDD, pp 226-231, Aug. 1996.
[21] A. Hinneburg and D. A. Keim, “An efficient approach to clustering in large multimedia databases with noise”, KDD'98, Aug. 1998.
[22] G. Sheikholeslami, S. Chatterjee, and A. Zhang, “WaveCluster: A multi-resolution clustering approach for very large spatial databases”, 24th Int. Conf. VLDB, pp 24-27, Aug. 1998.
[23] R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, “Automatic subspace clustering of high dimensional data for data mining applications”, SIGMOD’98, pp 94-105, June 1998.
[24] U. Fayyad, K.B. Irani., “Multi-interval discretization of continuos-value attributes as preprocessing for classification learning”, 13th Int. Join Conference on Artificial Intelligence, pp 1022-1027, 1993.
[25] Gennari, J. H., Langley, P., and Fisher, D. H. 1989. “Models of incremental concept formation” Artificial Intelligence, 40: pp 11-61, 1989.
[26] P. Cheeseman, J. Stutz, “Bayesian classification (AutoClass): Theory and results”, in Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, & Ramasamy Uthurusamy, Eds.Advances, Advances in Knowledge Discovery and Data Mining , 1996.
[27] Jain, A.K. and Dubes, R.C., “Algorithms for clustering data”, Prentice Hall, 1988.
[28] Anderberg, M. R., “Cluster analysis for applications”, Academic Press, Inc., 1973.
[29] Salton, G., “Developments in automatic text retrieval”, Science 253, pp 974–980, 1991.
[30] H´ajek, P., Havel, I., and Chytil, M., “The GUHA method of automatic hypotheses determination”, Computing, 1: pp293–308, 1966.
[31] H´ajek, P. and Havranek, T., “On generation of inductive hypotheses”, Int. J. Man-Machine Studies, 9: pp 415–438, 1977.
[32] Agrawal, R., Imielinski, T., and Swami, A., “Mining association rules between sets of items in large databases”, ACM SIGMOD Conference on Management of Data, pp. 207–216, 1993.
[33] Agarwal, R.C., Aggarwal, C.C., and Prasad, V., “Depth first generation of long patterns”, 6th ACM SIGKDD Conference on KDD, Boston, MA, pp. 108–118. 2000.
[34] Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, “Analysis of recommendation algorithms for E-Commerce”, EC’00, pp 17-20, October, 2000. |
[Endnote RIS 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
Google bookmarks
del.icio.us
hemidemi
facebook
twitter
google
reddit