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.

@inproceedings{melville2002contentboosted, author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass}, title = {Content-boosted Collaborative Filtering for Improved Recommendations}, booktitle = {Eighteenth National Conference on Artificial Intelligence}, publisher = {American Association for Artificial Intelligence}, address = {Menlo Park, CA, USA}, year = {2002}, pages = {187--192}, url = {http://dl.acm.org/citation.cfm?id=777092.777124}, isbn = {0-262-51129-0}, keywords = {collaborative, content, filtering, hybrid, recommender}, abstract = {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.} }

%0 = inproceedings %A = Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass %B = Eighteenth National Conference on Artificial Intelligence %C = Menlo Park, CA, USA %D = 2002 %I = American Association for Artificial Intelligence %T = Content-boosted Collaborative Filtering for Improved Recommendations %U = http://dl.acm.org/citation.cfm?id=777092.777124