Abstract
Eigentaste is a collaborative filtering algorithm that uses universal queries to elicit real-valued user ratings on a common set of items and applies principal component analysis (PCA) to the resulting dense subset of the ratings matrix. PCA facilitates dimensionality reduction for offline clustering of users and rapid computation of recommendations. For a database of n users, standard nearest-neighbor techniques require O(n) processing time to compute recommendations, whereas Eigentaste requires O(1) (constant) time. We compare Eigentaste to alternative algorithms using data from Jester, an online joke recommending system.
Jester has collected approximately 2,500,000 ratings from 57,000 users. We use the Normalized Mean Absolute Error (NMAE) measure to compare performance of different algorithms. In the Appendix we use Uniform and Normal distribution models to derive analytic estimates of NMAE when predictions are random. On the Jester dataset, Eigentaste computes recommendations two orders of magnitude faster with no loss of accuracy. Jester is online at: http://eigentaste.berkeley.edu
Article PDF
Similar content being viewed by others
References
Albaum G, Best R and Hawkins D (1981) Continuous vs discrete semantic differential ratings scales. Psychological Reports, 49: 90-97.
Arrow KJ (1963) Social Choice and Individual Values, 2nd ed. Yale University Press.
Billsus D and Pazzani M (1998) Learning collaborative information filters. Machine Learning. In: Proceedings of the Fifteenth International Conference (ICML'98), Madison, WI, USA,24-27 July 1998. Morgan Kaufmann Publishers, San Francisco, pp. 46-54.
Breese, Heckermen and Kadie (1998) Empirical analysis of predictive algorithms for collaborative filtering. Microsoft Research Technical Report, (MSR-TR-98-12).
Chislenko A et al. (2000) US Patent 6092049: Method and apparatus for efficiently recommending items using automated collaborative filtering and feature-guided automated collaborative filtering.
Dasarathy BV (1991) NN Pattern Classification Techniques. IEEE Computer Society Press, CA.
Deerwester S, Dumais S, Furnas G, Landauer T and Harshman R (1990) Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6): 391-407.
Delgado JA (2000) Agent-Based Information Filtering and Recommender Systems on the Internet. PhD Thesis, Nagoya Institute of Technology.
Ding CHQ (1999) A similarity-based probability model for latent semantic indexing. In: Proceedings of 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, August 1999. ACM, New York, NY, pp. 58-65.
Goldberg D, Nichols D, Oki B and Terry D (1992) Using collaborative filtering to weave an information tapestry. Communication of the ACM, 35(12): 61-70.
Gupta D and Goldberg K (1999) Jester 2.0: A linear time collaborative filtering algorithm applied to jokes. In: Proceedings of 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, August 1999. ACM, New York, NY, pp. 291-292.
Herlocker J, Konstan J, Borchers A and Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, August 1999. ACM, New York, NY, pp. 230-237.
Herlocker J, Konstan J, and Riedl J (2000) Explaining collaborative filtering recommendations. In: Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, December 2-6, 2000.
Hey J (1989) US Patent 4870579: System and method of predicting subjective reactions.
Hofmann T (1999) Probabilistic latent semantic indexing. In: Proceedings of 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, August 1999. ACM, New York, NY, pp. 50-57.
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. Journal of Eduational Psychology, 24: 417-441.
Jackson JE (1991) A User Guide to Principal Components: A Problem Solving Approach. John Wiley and Sons, New York.
Konstan JA and Bharat K (1996) Integrated personal and community recommendations in collaborative filtering. CSCW Workshop.
Konstan J, Miller B, Maltz D, Herlacker J, Gordon L and Riedl J (1997) Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM, 40(3): 77-87.
Krishnaiah PR and Kanal LN (1982) Classification, pattern recognition and reduction in dimensionality. In: Handbook of Statistics 2. North-Holland Publishing Company, Amsterdam, New York, Oxford.
Landauer T, Littman M and Bell Communications Research (Bellcore) (1994) US Patent 5301109: Computerized cross-language document retrieval using latent semantic indexing.
Maltz DA and Ehlrich K (1995) Pointing the way: Active collaborative filtering. Chi'95 Proceedings Papers.
Nilsen D, ed. International Journal of Humor Research. Walter de Gruyter Inc., New York. (2000).
Pearson K (1901) On lines and planes of closest fit to systems of points in space. Phil. Mag., 2: 559-572.
Pennock DM and Horvitz E (1999a) Analysis of the axiomatic foundations of collaborative filtering. In: AAAI Workshop on Artificial Intelligence for Electronic Commerce, Orlando, Florida, National Conference on Arti-ficial Intelligence.
Pennock DM and Horvitz E (1999b) Collaborative filtering by personality diagnosis:Ahybrid memory-and modelbased approach. In: IJCAI Workshop on Machine Learning for Information Filtering, Stockholm, Sweden, International Joint Conference on Artificial Intelligence.
Pryor M (1998) The effects of singular value decomposition on collaborative filtering. Dartmouth College CS Technical Report, PCS-TR 98-338.
Resnick P, Iacovou N, Suchak M, Bergstrom P and Riedl J (1994) Grouplens: An open architecture for collaborative filtering of netnews. Proceedings of the ACM Conference on Computer Supported Cooperative Work.
Rich E (1979) User Modeling via Stereotypes. Cognitive Science, 3: 335-366.
Shardanand U and Maes P (1995) Social information filtering: Algorithms for automating word of mouth. ACM Conference on Computer Human Interaction (CHI).
Sarwar BM, Karypis G, Konstan J and Riedl JT (2000) Application of dimensionality reduction in recommender systems-a case study. ACM Conference on E-Commerce.
Varian H and Resnick P (1997) Special issue on cf and recommender systems Communication of the ACM, 40(3).
Ziv A (1984) Personality and Sense of Humor. Springer Publishing Co., New York.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Goldberg, K., Roeder, T., Gupta, D. et al. Eigentaste: A Constant Time Collaborative Filtering Algorithm. Information Retrieval 4, 133–151 (2001). https://doi.org/10.1023/A:1011419012209
Issue Date:
DOI: https://doi.org/10.1023/A:1011419012209