TiVo: making show recommendations using a distributed collaborative filtering architecture

ABSTRACT

We describe the TiVo television show collaborative recommendation system which has been fielded in over one million TiVo clients for four years. Over this install base, TiVo currently has approximately 100 million ratings by users over approximately 30,000 distinct TV shows and movies. TiVo uses an item-item (show to show) form of collaborative filtering which obviates the need to keep any persistent memory of each user's viewing preferences at the TiVo server. Taking advantage of TiVo's client-server architecture has produced a novel collaborative filtering system in which the server does a minimum of work and most work is delegated to the numerous clients. Nevertheless, the server-side processing is also highly scalable and parallelizable. Although we have not performed formal empirical evaluations of its accuracy, internal studies have shown its recommendations to be useful even for multiple user households. TiVo's architecture also allows for throttling of the server so if more server-side resources become available, more correlations can be computed on the server allowing TiVo to make recommendations for niche audiences.

REFERENCES

• 1: Charu C. Aggarwal , Joel L. Wolf , Kun-Lung Wu , Philip S. Yu, Horting hatches an egg: a new graph-theoretic approach to collaborative filtering, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.201-212, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312230]
• 2: Daniel Billsus , Michael J. Pazzani, Learning Collaborative Information Filters, Proceedings of the Fifteenth International Conference on Machine Learning, p.46-54, July 24-27, 1998
• 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: John Canny, Collaborative filtering with privacy via factor analysis, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564419]
• 5: Peter Cheeseman , John Stutz, Bayesian classification (AutoClass): theory and results, Advances in knowledge discovery and data mining, American Association for Artificial Intelligence, Menlo Park, CA, 1996
• 6: Digital Equipment Research Center. http://www.research.digital.com/SRC/EachMovie/.
• 7: William W. Cohen , Wei Fan, Web-collaborative filtering: recommending music by crawling the Web, Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking, p.685-698, June 2000, Amsterdam, The Netherlands
• 8: Richard O. Duda , Peter E. Hart , David G. Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000
• 9: Everitt B. S. (2002). The Cambridge Dictionary of Statistics. Cambridge Press.
• 10: David Goldberg , David Nichols , Brian M. Oki , Douglas Terry, Using collaborative filtering to weave an information tapestry, Communications of the ACM, v.35 n.12, p.61-70, Dec. 1992 [doi>10.1145/138859.138867]
• 11: Will Hill , Larry Stead , Mark Rosenstein , George Furnas, Recommending and evaluating choices in a virtual community of use, Proceedings of the SIGCHI conference on Human factors in computing systems, p.194-201, May 07-11, 1995, Denver, Colorado, United States [doi>10.1145/223904.223929]
• 12: Kalervo Järvelin , Jaana Kekäläinen, IR evaluation methods for retrieving highly relevant documents, Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.41-48, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345545]
• 13: Nichols D. (1997). Implicit rating and filtering. In Proceedings of the Fifth DELIOS Workshop on Filtering and Collaborative Filtering, Budapest, Hungary.
• 14: Gerard Salton , Christopher Buckley, Term-weighting approaches in automatic text retrieval, Information Processing and Management: an International Journal, v.24 n.5, p.513-523, 1988 [doi>10.1016/0306-4573(88)90021-0]
• 15: Badrul Sarwar , George Karypis , Joseph Konstan , John Reidl, Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, p.285-295, May 01-05, 2001, Hong Kong, Hong Kong [doi>10.1145/371920.372071]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms

Keywords:
clustering clickstreams, collaborative-filtering