PUMA publications for /author/Prem%20Melvillehttps://puma.uni-kassel.de/author/Prem%20MelvillePUMA RSS feed for /author/Prem%20Melville2024-03-28T20:57:56+01:00Content-boosted Collaborative Filtering for Improved Recommendationshttps://puma.uni-kassel.de/bibtex/2a4917f0299f48e403966a8003ebd50be/hothohotho2015-02-16T17:25:55+01:00hybrid recommender collaborative filtering content <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Prem Melville" itemprop="url" href="/author/Prem%20Melville"><span itemprop="name">P. Melville</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Raymod J. Mooney" itemprop="url" href="/author/Raymod%20J.%20Mooney"><span itemprop="name">R. Mooney</span></a></span>, и <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ramadass Nagarajan" itemprop="url" href="/author/Ramadass%20Nagarajan"><span itemprop="name">R. Nagarajan</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Eighteenth National Conference on Artificial Intelligence</span>, </em></span><em>стр. <span itemprop="pagination">187--192</span>. </em><em>Menlo Park, CA, USA, </em><em><span itemprop="publisher">American Association for Artificial Intelligence</span>, </em>(<em><span>2002<meta content="2002" itemprop="datePublished"/></span></em>)Mon Feb 16 17:25:55 CET 2015Menlo Park, CA, USAEighteenth National Conference on Artificial Intelligence187--192Content-boosted Collaborative Filtering for Improved Recommendations2002hybrid recommender collaborative filtering content 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.