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Abstracts - 1997 ANNUAL TECHNICAL CONFERENCE
Experience with GroupLens: Making Usenet Useful Again
Bradley N. Miller, John T. Riedl, and Joseph A. Konstan
Department of Computer Science, University of Minnesota
Abstract
Collaborative filetering attempts to alleviate information overload by offering
recommendations on whether information is valuable based on the
opinions of those who have already evaluated it. Usenet news is an
information source whose value is being severely diminished by the
volume of low-quality and uninteresting information posted in its
newsgroups. The GroupLens system applies collaborative filtering to
Usenet news to demonstrate how we can restore the value of Usenet news
by sharing our judgements of articles, with our identities protected by
pseudonyms.
This paper extends the original GroupLens work by
reporting on a significantly enhanced system and the results of a seven
week trial with 250 users and over 20,000 news articles. GroupLens has
an open and flexible architecture that allows easy integration of new
newsreader clients and ratings bureaus. We show ratings and prediction
profiles for three newsgroups, and assess the accuracy of the
predictions.
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