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.
- Aggarwal 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. Google ScholarDigital Library
- Billsus, D. and Pazzani, M. (1998). Learning Collaborative Information Filters, ICML 1998: 46--54. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Canny J. (2002). Collaborative Filtering with Privacy via Factor Analysis, ACM SIGIR, Tampere, Finland. Google ScholarDigital Library
- Cheeseman, P. and Stutz, J. (1995). Bayesian Classification (AutoClass): Theory and Results. In Fayyad, U., Piatetsky-Shapiro, G., Smyth, P. and Uthurusamy, R., eds., Advances in Knowledge Discovery and Data Mining, 153--180. AAAI Press. Google ScholarDigital Library
- Digital Equipment Research Center. http://www.research.digital.com/SRC/EachMovie/.Google Scholar
- Cohen, W.W. and Fan, W. (2000). Web-Collaborative Filtering: Recommending Music by Crawling the Web. In Proceedings of WWW9. Google ScholarDigital Library
- Duda, R.O. and Hart, P.E. (1972). Pattern Classification and Scene Analysis. Wiley, New York. Google ScholarDigital Library
- Everitt B. S. (2002). The Cambridge Dictionary of Statistics. Cambridge Press.Google Scholar
- Goldberg, D., Nichols, D., Oki, B.M. and Terry, D. (1992). Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM, 35, 12 pp. 61--70. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Järvelin, K. and Kekäläinen J. (2000). IR evaluation methods for retrieving highly relevant documents. In Nicholas J. Belkin, Peter Ingwersen, and Mun-Kew Leong, editors, Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 41--48. Google ScholarDigital Library
- Nichols D. (1997). Implicit rating and filtering. In Proceedings of the Fifth DELIOS Workshop on Filtering and Collaborative Filtering, Budapest, Hungary.Google Scholar
- Salton, G. and Buckley, C. (1988). Term weighting approaches in automatic text retrieval. In Information Processing and Management, 24, 5, pp. 513--523. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. Item-Based Collaborative Filtering Recommendation Algorithms. In Proceedings of WWW10, May 2001. Google ScholarDigital Library
Index Terms
- TiVo: making show recommendations using a distributed collaborative filtering architecture
Recommendations
Domain ranking for cross domain collaborative filtering
UMAP'12: Proceedings of the 20th international conference on User Modeling, Adaptation, and PersonalizationIn recommendation systems a variation of the cold start problem is a situation where the target user has few-to-none item ratings belonging to the target domain (e.g., movies) to base recommendations on. One way to overcome this is by basing ...
Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender SystemsThe item cold-start problem is inherent to collaborative filtering (CF) recommenders where items and users are represented by vectors in a latent space. It emerges since CF recommenders rely solely on historical user interactions to characterize their ...
Music recommendations for groups of users
ImmersiveMe '13: Proceedings of the 2013 ACM international workshop on Immersive media experiencesThis paper presents an algorithm capable of providing meaningful recommendations to small sets of users. We consider not only rating patterns, bias tendencies, and temporal fluctuations, but also group-leaders. The approach here presented intends to ...
Comments