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    Melville, P., Mooney, R.J. & Nagarajan, R. Content-boosted Collaborative Filtering for Improved Recommendations 2002 Eighteenth National Conference on Artificial Intelligence, pp. 187-192  inproceedings URL 
    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.
    BibTeX:
    @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},
      year = {2002},
      pages = {187--192},
      url = {http://dl.acm.org/citation.cfm?id=777092.777124}
    }
    

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