TIVo: Making show recommendations using a distributed collaborative filtering architecture

KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2004, Pages 394-401

  Ali, Kamal ^a   Van Stam, Wijnand ^b  

^a Yahoo   (United States)

^b TiVo   (United States)

Author keywords

Clustering Clickstreams; Collaborative Filtering

Indexed keywords

CLUSTERING CLICKSTREAMS; COLLBAORATIVE FILTERING ARCHITECTURE; SERVER-SIDE PROCESSING; TELEVISION (TV) CHANNELS;

CLIENT SERVER COMPUTER SYSTEMS; COMMUNICATION CHANNELS (INFORMATION THEORY); COMPUTER SUPPORTED COOPERATIVE WORK; DISTRIBUTED COMPUTER SYSTEMS; MOTION PICTURES; SERVERS; SIGNAL FILTERING AND PREDICTION; SIGNAL PROCESSING;

TELEVISION BROADCASTING;

EID: 12244275962     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Agganval C.C., Wolf J.L., Wu K-L. and Yu P.S. Horting. (1999). Hatches an Egg: A New Graph-Theoretic Approach to Collaborative Filtering. In KDD'99, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 201 -212. ACM Press.
2. Billsus, D. and Pazzani, M. (1998). Learning Collaborative Information Filters, ICML 1998: 46-54.
3. Breese J.S., Heckerman D and Kadie C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI. Morgan-Kaufmann.
4. Canny J. (2002). Collaborative Filtering with Privacy via Factor Analysis, ACM SIGIR, Tampere, Finland.
5. Cheeseman, P. and Stutz, J. (1995). Bayesian Classification (AutoClass): Theory and Results. In Fayyad, U., Piatetsky-Shapiro, G., Smyth, P. and Uthurusamy, R., ed.s, Advances in Knowledge Discovery and Data Mining, 153-180. AAAI Press.
6. Digital Equipment Research Center. http://www.research.digital.com/SRC/ EachMovie/.
7. Cohen, W.W. and Fan, W. (2000). Web-Collaborative Filtering: Recommending Music by Crawling the Web. In Proceedings of WWW9.
8. Duda, R.O. and Hart, P.E. (1972). Pattern Classification and Scene Analysis. Wiley, New York.
9. Everitt B. S. (2002). The Cambridge Dictionary of Statistics. Cambridge Press.
10. Goldberg, D., Nichols, D., Oki, B.M. and Terry, D. (1992). Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM, 35, 12pp. 61-70.
11. Hill, W., Stead, L., Rosenstein, M., and Furnas, G. (1995). Recommending and evaluating choices in a virtual community of use. In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, 194-201.
12. rdAnnual International ACM SIGIR Conference on Research and Development in Information Retrieval, 41-48.
13. Nichols D. (1997). Implicit rating and filtering. In Proceedings of the Fifth DELIOS Workshop on Filtering and Collaborative Filtering, Budapest, Hungary.
14. Salton, G. and Buckley, C. (1988). Term weighting approaches in automatic text retrieval. In Information Processing and Management, 24, 5, pp. 513-523.
15. Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. Item-Based Collaborative Filtering Recommendation Algorithms. In Proceedings of WWW 10, May 2001.