research-article

Eigentaste: A Constant Time Collaborative Filtering Algorithm

Authors:

Theresa Roeder,

Chris PerkinsAuthors Info & Claims

Information Retrieval, Volume 4, Issue 2

Pages 133 - 151

https://doi.org/10.1023/A:1011419012209

Published: 01 July 2001 Publication History

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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Index Terms

Eigentaste: A Constant Time Collaborative Filtering Algorithm
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Information Retrieval

Information Retrieval Volume 4, Issue 2

Jul 2001

95 pages

ISSN:1386-4564

Issue’s Table of Contents

© Kluwer Academic Publishers 2001.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2001

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