Eigentaste: A Constant Time Collaborative Filtering Algorithm

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Eigentaste: A Constant Time Collaborative Filtering Algorithm (2000)

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by Ken Goldberg , Theresa Roeder , Dhruv Gupta , Chris Perkins

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@MISC{Goldberg00eigentaste:a,
    author = {Ken Goldberg and Theresa Roeder and Dhruv Gupta and Chris Perkins},
    title = {Eigentaste: A Constant Time Collaborative Filtering Algorithm},
    year = {2000}
}

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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 ...

Keyphrases

constant time collaborative filtering algorithm component analysis different algorithm dense subset rating matrix universal query normalized mean absolute error normal distribution model whereas eigentaste rapid computation offline clustering common set online joke real-valued user rating analytic estimate standard nearest-neighbor technique jester dataset processing time collaborative filtering algorithm dimensionality reduction

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