@inproceedings{melville2002contentboosted,
  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.},
  acmid = {777124},
  address = {Menlo Park, CA, USA},
  author = {Melville, Prem and Mooney, Raymod J. and Nagarajan, Ramadass},
  booktitle = {Eighteenth National Conference on Artificial Intelligence},
  interhash = {985028099c1a29f116ad7434005895ac},
  intrahash = {a4917f0299f48e403966a8003ebd50be},
  isbn = {0-262-51129-0},
  location = {Edmonton, Alberta, Canada},
  numpages = {6},
  pages = {187--192},
  publisher = {American Association for Artificial Intelligence},
  title = {Content-boosted Collaborative Filtering for Improved Recommendations},
  url = {http://dl.acm.org/citation.cfm?id=777092.777124},
  year = 2002
}