Experiences with GroupLens: marking usenet useful again
作者： John T. Riedl , Joseph A. Konstan , Bradley N. Miller
DOI:
关键词: Internet privacy 、 Information source 、 Information overload 、 Computer science 、 World Wide Web 、 Collaborative filtering
摘要: Collaborative filetering attempts to alleviate information overload by offering recommendations on whether is valuable based the opinions of those who have already evaluated it. Usenet news an source whose value being severely diminished volume low-quality and uninteresting posted in its newsgroups. The GroupLens system applies collaborative filtering demonstrate how we can restore sharing our judgements articles, with identities protected pseudonyms. This paper extends original work reporting a significantly enhanced results seven week trial 250 users over 20,000 articles. has open flexible architecture that allows easy integration new newsreader clients ratings bureaus. We show prediction profiles for three news-groups, assess accuracy predictions.
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