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 to 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.

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In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), pp. 187-192, Edmonton, Alberta 2002.

@InProceedings{melville:aaai02, title={Content-Boosted Collaborative Filtering for Improved Recommendations}, author={Prem Melville and Raymond J. Mooney and Ramadass Nagarajan}, booktitle={Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02)}, address={Edmonton, Alberta}, pages={187-192}, url="http://www.cs.utexas.edu/users/ai-lab?melville:aaai02", year={2002} }