Towards a more comprehensive comparison of collaborative filtering algorithms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2476, Issue , 2002, Pages 248-253

  González Caro, Cristina N ^a   Calderón Benavides, Maritza L ^a   De Pérez Alcázar, José J ^a   Garcáa Díaz, Juan C ^a   Delgado, Joaquin ^b  

^a UNIVERSIDAD AUTÓNOMA DE BUCARAMANGA   (Colombia)

^b TripleHop Technologies   (United States)

Author keywords

Aspect model; Collaborative filtering; Dependency networks; Memory based models; Online learning; Support vector machines

Indexed keywords

ALGORITHMS; E-LEARNING; INFORMATION RETRIEVAL; SUPPORT VECTOR MACHINES;

AMOUNT OF INFORMATION; ASPECT MODEL; COLLABORATIVE FILTERING ALGORITHMS; COMPREHENSIVE COMPARISONS; DEPENDENCY NETWORKS; EVALUATION METRICS; ONLINE LEARNING; SUPPORT VECTOR MACHINES ALGORITHMS;

COLLABORATIVE FILTERING;

EID: 36248994270     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45735-6_22     Document Type: Conference Paper

References (17)

1. D. Billsus and M. Pazzani. Learning collaborative information filters. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 43–52, 1998.
2. Avrim Blum. Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain. Proc. 12th International Conference on Machine Learning, (240):265–23, 1997.
3. J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, Madison, WI, pages 43–52. Morgan Kaufman Eds., 1998.
4. M. Brown, W. Grundy, D. Lin, N. Cristianini, C. Sugnet, M. Ares, and D. Haussler. Support vector machine classification of microarray gene expression data. Technical report UCSC-CRL99 -09, Department of Computer Science, University of California, Santa Cruz, 1999.
5. C. Burgues. A tutorial on Support Vector Machines for Pattern Recognition. Kluwer Academic Publishers, Boston, 1998.
6. C. Chang and C. Lin. Libsvm: a library for support vector machines (version 2.33). Technical report, Department of Computer Science and Information Engineering, National Taiwan University.
7. J. Delgado and N. Ishii. Memory-based weighted-majority prediction for recommender systems. In Proceedings of the ACM SIGIR-99, Recommender Systems Workshop, August 1999, UC Berkeley, pages 1–5, 1999.
8. D. Fisher, K. Hildrum, J. Hong, M. Newman, M. Thomas, and R. Vuduc. Swami (poster session): a framework for collaborative filtering algorithm development and evaluation. In Proceedings of the 22nd Annual International ACM SIGIR, 2000, Athens, Greece, pages 366–368. ACM, 2000.
9. K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Technical report m00/41., University of California, Berkeley, 2000.
10. D. Heckerman, D. Chickering, C. Meek, R. Rounthwaite, and C. Kadie. Dependency networks for inference, collaborative filtering and data visualization. Journal of Machine Learning Research, 1:49–75, 2000.
11. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR, August 15-19, 1999, Berkeley, CA, USA, pages 230–237. ACM, 1999.
12. T. Hofmann and J. Puzicha. Latent class model from collaborative filtering. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Morgan Kaufman Eds., 1999.
13. N. Littelstone and M.K. Warmuth. The weighted-majority algorithm. Information and Computation, 108(2):212–261, 1994.
14. P. McJonese. Eachmovie collaborative filtering data set. DEC Systems Research Center., 1997.
15. P. Resnick, N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl. GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, pages 175–186, Chapel Hill, North Carolina, 1994. ACM.
16. A. Shein and L. Popescul, A. Ungar. Pennaspect: A two-way aspect model implementation. Technical Report MS-CIS-01-25, The University of Pennsylvania., 2001.
17. I. Witten and E. Frank. Data Mining, Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, 2000.