Scalable Collaborative Filtering Approaches for Large Recommender Systems
Scalable Collaborative Filtering Approaches for Large Recommender Systems
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DOI：
10.5555/1577069.1577091
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
G. Takács;I. Pilászy;B. Németh;D. Tikk
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文献类型：
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作者：
G. Takács;I. Pilászy;B. Németh;D. Tikk
协同过滤（CF）使用已知的用户评分的项目已被证明是有效的预测用户的偏好在项目选择。这个蓬勃发展的机器学习子领域在20世纪90年代后期随着使用推荐系统的在线服务的普及而流行起来，如亚马逊，雅虎！音乐和Netflix。CF方法通常被设计用于处理非常大的数据集。因此，方法的可扩展性至关重要。在这项工作中，我们提出了各种可扩展的解决方案，这些解决方案针对Netflix Prize数据集进行了验证，该数据集是目前最大的公开数据集。首先，我们提出了各种基于矩阵分解（MF）的技术。其次，提出了一种MF的邻域校正方法，该方法有效地结合了MF的全局性和基于邻域的方法的局部性。在实验部分，我们首先报告了一些实现问题，我们建议如何有效地进行参数优化的MF。然后，我们表明，所提出的可扩展的方法相比，与现有的预测精度和/或所需的训练时间。最后，我们报告了在MovieLens和Jester数据集上进行的一些实验。
The collaborative filtering (CF) using known user ratings of items has proved to be effective for predicting user preferences in item selection. This thriving subfield of machine learning became popular in the late 1990s with the spread of online services that use recommender systems, such as Amazon, Yahoo! Music, and Netflix. CF approaches are usually designed to work on very large data sets. Therefore the scalability of the methods is crucial. In this work, we propose various scalable solutions that are validated against the Netflix Prize data set, currently the largest publicly available collection. First, we propose various matrix factorization (MF) based techniques. Second, a neighbor correction method for MF is outlined, which alloys the global perspective of MF and the localized property of neighbor based approaches efficiently. In the experimentation section, we first report on some implementation issues, and we suggest on how parameter optimization can be performed efficiently for MFs. We then show that the proposed scalable approaches compare favorably with existing ones in terms of prediction accuracy and/or required training time. Finally, we report on some experiments performed on MovieLens and Jester data sets.