Learning Collaborative Information Filters
Learning Collaborative Information Filters
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发表时间：
1998-07
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通讯作者：
Daniel Billsus;M. Pazzani
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作者：
Daniel Billsus;M. Pazzani
基于其他用户对这些项目的评分来预测用户喜欢的项目已经成为互联网上的许多推荐服务所采用的成熟策略。虽然这可以被视为一个分类问题，但到目前为止提出的算法并没有利用机器学习文献的结果。我们提出了一种协作过滤任务的表示，该表示允许应用几乎任何机器学习算法。我们找出了当前协同过滤技术的缺点，并提出将学习算法与特征提取技术结合使用，专门解决了以前方法的局限性。我们性能最好的算法是基于用户评分初始矩阵的奇异值分解，利用潜在结构，基本上消除了用户为了成为彼此偏好的预测者而对共同项目进行评分的需要。我们在一个大型的运动图像用户评分数据库上对所提出的算法进行了评估，发现我们的方法明显优于当前的协作过滤算法。
Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algorithms proposed thus far do not draw on results from the machine learning literature. We propose a representation for collaborative filtering tasks that allows the application of virtually any machine learning algorithm. We identify the shortcomings of current collaborative filtering techniques and propose the use of learning algorithms paired with feature extraction techniques that specifically address the limitations of previous approaches. Our best-performing algorithm is based on the singular value decomposition of an initial matrix of user ratings, exploiting latent structure that essentially eliminates the need for users to rate common items in order to become predictors for one another's preferences. We evaluate the proposed algorithm on a large database of user ratings for motion pictures and find that our approach significantly outperforms current collaborative filtering algorithms.