TY - CONF AU - Melville, Prem AU - Mooney, Raymod J. AU - Nagarajan, Ramadass A2 - T1 - Content-boosted Collaborative Filtering for Improved Recommendations T2 - Eighteenth National Conference on Artificial Intelligence PB - American Association for Artificial Intelligence C1 - Menlo Park, CA, USA PY - 2002/ CY - VL - IS - SP - 187 EP - 192 UR - http://dl.acm.org/citation.cfm?id=777092.777124 DO - KW - hybrid KW - recommender KW - collaborative KW - filtering KW - content L1 - SN - 0-262-51129-0 N1 - N1 - AB - 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. ER -