Michael Pazzani Department of Information and Computer Science, University of California, Irvine Irvine, CA 92697-3425 firstname.lastname@example.orgDate Donated: October 20, 1998
The problem is to predict user ratings for web pages (within a subject category). The HTML source of a web page is given. Users looked at each web page and inidated on a 3 point scale (hot medium cold) 50-100 pages per domain. However, this is realistic because we want to learn user profiles from as few examples as possible so that users have an incentitive to rate pages.
The accuracy of predicting ratings is reported in early publications. Later publications used the precision at top N or the F-measure.
Pazzani M., Billsus, D. (1997). Learning and Revising User Profiles: The identification of interesting web sites. Machine Learning 27, 313-331
Pazzani, M., Muramatsu J., Billsus, D. (1996). Syskill & Webert: Identifying interesting web sites. Proceedings of the National Conference on Artificial Intelligence, Portland, OR. PDF