%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, <i>Content-Boosted Collaborative Filtering</i>, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.