Content-Boosted Collaborative Filtering
Content-Boosted Collaborative Filtering
复制标题
内容增强的协同过滤
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
Prem Melville and Raymond J. Mooney and Ramadass Nagarajan
中科院分区：
文献类型：
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作者：
Prem Melville and Raymond J. Mooney and Ramadass Nagarajan
大多数推荐系统使用协同过滤或基于内容的方法来预测用户感兴趣的新项目。虽然这两种方法都有自己的优点，但单独来看，它们在许多情况下都不能提供好的建议。结合这两种方法的组成部分，混合推荐系统可以克服这些缺点。在本文中，我们提出了一个优雅而有效的框架来结合内容和协作。我们的方法使用基于内容的预测器来增强现有用户数据，然后通过协作过滤提供个性化建议。我们提出的实验结果表明，这种方法，内容增强协同过滤，如何比纯粹的基于内容的预测器，纯粹的协作过滤和朴素的混合方法表现得更好。我们还讨论了提高混合系统性能的方法。
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 e ective framework for combining content and collaboration. Our approach uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative ltering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative lter, and a naive hybrid approach. We also discuss methods to improve the performance of our hybrid system.