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Experience with GroupLens: Making Usenet Useful Again

Bradley N. Miller, John T. Riedl, and Joseph A. Konstan
Department of Computer Science, University of Minnesota


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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