Eigentaste: A Constant Time Collaborative Filtering Algorithm |
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| Authors: | Ken Goldberg Theresa Roeder Dhruv Gupta Chris Perkins |
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| Institution: | (1) IEOR and EECS Departments, University of California, Berkeley, CA, USA;(2) IEOR Department, University of California, Berkeley, CA, USA |
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| 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 |
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| Keywords: | recommender systems collaborative filtering dimensionality reduction jokes |
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