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%0 Conference Proceedings
%A Melville, Prem; Mooney, Raymod J. & Nagarajan, Ramadass
%D 2002
%T Content-boosted Collaborative Filtering for Improved Recommendations
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%B Eighteenth National Conference on Artificial Intelligence
%C Menlo Park, CA, USA
%I American Association for Artificial Intelligence
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%N 
%P 187--192
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%@ 0-262-51129-0
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%3 inproceedings
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%F melville2002contentboosted
%K collaborative, content, filtering, hybrid, recommender
%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.
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%U http://dl.acm.org/citation.cfm?id=777092.777124
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