%0 Conference Paper %1 melville2002contentboosted %A Melville, Prem %A Mooney, Raymod J. %A Nagarajan, Ramadass %B Eighteenth National Conference on Artificial Intelligence %C Menlo Park, CA, USA %D 2002 %I American Association for Artificial Intelligence %K collaborative content filtering hybrid recommender %P 187--192 %T Content-boosted Collaborative Filtering for Improved Recommendations %U http://dl.acm.org/citation.cfm?id=777092.777124 %X Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.