Content-boosted Collaborative Filtering for Improved Recommendations
Melville, P.; Mooney, R. J. & Nagarajan, R.
, 'Eighteenth National Conference on Artificial Intelligence', American Association for Artificial Intelligence, Menlo Park, CA, USA, 187-192 (2002) [pdf]
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.