Title: Learning Collaborative Information Filters
Daniel Billsus and Michael J. Pazzani
Department of Information and Computer Science
University of California, Irvine
Irvine, CA 92697-3425
{dbillsus, pazzani}@ics.uci.edu
Abstract:
Predicting items a user would like on the basis of other users ratings for
these items has become a well-established strategy adopted by many
recommendation services on the Internet. Although this can be seen as a
classification problem, algorithms proposed thus far do not draw on results
from the machine learning literature. We propose a representation for
collaborative filtering tasks that allows the application of virtually any
machine learning algorithm. We identify the shortcomings of current
collaborative filtering techniques and propose the use of learning
algorithms paired with feature extraction techniques that specifically
address the limitations of previous approaches. Our best-performing
algorithm is based on the singular value decomposition of an initial matrix
of user ratings, exploiting latent structure that essentially eliminates the
need for users to rate common items in order to become predictors for one
another's preferences. We evaluate the proposed algorithm on a large
database of user ratings for motion pictures and find that our approach
significantly outperforms current collaborative filtering algorithms.
Keywords:
collaborative filtering, intelligent agents, classification, feature
selection, latent semantic indexing, singular value decomposition.
Email address of contact author:
dbillsus@ics.uci.edu
Phone number of contact author:
(714) 824-3491