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- Datasource
- Association for Computing Machinery
- Copyright
- Copyright © 2002 by ACM Inc.
- ISBN
- 1-58113-459-2
- doi
- 10.1145/502716.502737
- Publisher site
- See Article on Publisher Site
Abstract
Getting to Know You: Learning New User Preferences in Recommender Systems Al Mamunur Rashid, Istvan Albert, Dan Cosley, Shyong K. Lam, Sean M. McNee, Joseph A. Konstan, John Riedl GroupLens Research Project Department of Computer Science and Engineering University of Minnesota Minneapolis, MN 55455 USA {arashid, ialbert, cosley, lam, mcnee, konstan, riedl}@cs.umn.edu Recommender systems help people make decisions in these complex information spaces. Recommenders suggest to the user items that she may value based on knowledge about her and the space of possible items. A news service, for example, might remember the articles a user has read. The next time she visits the site, the system can recommend new articles to her based on the ones she has read before. Collaborative filtering is one technique for producing recommendations. Given a domain of choices (items), users can express their opinions (ratings) of items they have tried before. The recommender can then compare the user ™s ratings to those of other users, find the œmost similar users based on some criterion of similarity, and recommend items that similar users have liked in the past. When new users come along, however, the system knows nothing about them. This is called