Empirical study of recommender systems using linear classifiers

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2035, Issue , 2001, Pages 16-27

  Iyengar, Vijay S ^a   Zhang, Tong ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DECISION TREES; FILTRATION; TREES (MATHEMATICS);

COMPUTATIONAL REQUIREMENTS; EMPIRICAL STUDIES; HISTORICAL DATA; LINEAR CLASSIFIERS; MODEL-BASED METHOD; MODEL-BASED OPC;

RECOMMENDER SYSTEMS;

EID: 84942938157     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-45357-1_5     Document Type: Conference Paper

References (20)

1. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of CSCW, 1994.
2. U. Shardanand and P. Maes. Social information filtering: Algorithms for automat- ing word of mouth. In Proceedings of CHI'95, 1995.
3. J.S. Breese, D. Heckerman, and C. Kadie. Emperical analysis of predictive al- gorithms for collaborative filtering. In Proceedings of Fourteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 1998.
4. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Analysis of recommendation algorithms for e-commerce. In Proceedings ACM E-Commerce 2000 Conference, 2000.
5. R. Hofmann and V. Tresp. Nonlinear markov networks for continuous variables. In Advances in Neural Information Processing Systems 9, editors: M. Mozer, M. Jordan and T. Petsche. MIT Press, 1997.
6. D. Heckerman, D. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Depen- dency networks for collaborative filtering and data visualization. In Proc. of 16th Conf. on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 2000.
7. L. Ungar and D. Foster. Clustering methods for collaborative filtering. In Workshop on Recommendation Systems at the Fifteenth National Conference on AI, 1998.
8. L. Ungar and D. Foster. A formal statistical approach to collaborative filtering. In CONALD'98, 1998.
9. T. Joachims. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the Tenth European Conference on Ma- chine Learning, pages 137-142, 1998.
10. C. Blake, E. Keogh, and C.J. Merz. UCI repository of machine learning databases. University of California, Irvine, Dept. of Information and Computer Science, URL=http://www.ics.uci.edu/-mlearn/MLRespository.html, 1998.
11. S. Glassman. Eachmovie data set. URL=http://research.compaq.com/SRC- /eachmovie/.
12. Yiming Yang and Christopher G. Chute. An example-based mapping method for text categorization and retrieval. ACM Transactions on Information Systems, 12:252-277, 1994.
13. B.D. Ripley. Pattern recognition and neural networks. Cambridge university press, 1996.
14. A. E. Hoerl and R. W. Kennard. Ridge regression: Biased estimation for nonorthog- onal problems. Technometrics, 12(1):55-67, 1970.
15. C. Cortes and V.N. Vapnik. Support vector networks. Machine Learning, 20:273-297, 1995.
16. V.N. Vapnik. Statistical learning theory. John Wiley & Sons, New York, 1998.
17. John C. Platt. Fast training of support vector machines using sequential minimal optimization. In Bernhard Scholkopf, Christopher J. C. Burges, and Alex J. Smola, editors, Advances in Kernel Methods: Support Vector Learning, chapter 12. The MIT press, 1999.
18. F.M.J. Willems, Y.M. Shtarkov, and T.J. Tjalkens. The context tree weighting method: Basic properties. IEEE Trans. on Inform. Theory, 41(3):653-664, 1995.
19. Tong Zhang. Compression by model combination. In Proceedings of IEEE Data Compression Conference, DCC'98, pages 319-328, 1998.
20. M. Kearns and Y. Mansour. A fast, bottom-up decision tree pruning algorithm with near optimal generalization. In Proceedings of the 15th International Conference on Machine Learning, pages 269-277, 1998.