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 recommendattions 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. We also discuss methods to improve the performance of our hybrid system.

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In Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001.

@InProceedings{melville:sigir01-wkshp, title={Content-Boosted Collaborative Filtering}, author={Prem Melville and Raymond J. Mooney and Ramadass Nagarajan}, booktitle={Proceedings of the SIGIR-2001 Workshop on Recommender Systems}, month={September}, address={New Orleans, LA}, url="http://www.cs.utexas.edu/users/ai-lab?melville:sigir01-wkshp", year={2001} }