Netflix Prize and SVD
Netflix Prize and SVD
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Netflix 奖和 SVD
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Gower
中科院分区：
文献类型：
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作者：
S. Gower
奇异值分解（SVD）在协同过滤领域变得非常流行。著名的 Netflix 奖的获奖作品有许多 SVD 模型，包括与受限玻尔兹曼机混合的 SVD++。使用这些方法，他们的准确率比 Netflix 现有算法提高了 10%。在本文中，我探讨了成功的推荐模型的不同方面。我还将探讨一些更突出的基于 SVD 的模型，例如迭代 SVD、SVD++ 和正则化 SVD。本文是为具有分解和线性代数基础知识的人设计的，并试图以大多数人都能理解的方式解释这些算法的工作原理。
Singular Value Decompositions (SVD) have become very popular in the field of Collaborative Filtering. The winning entry for the famed Netflix Prize had a number of SVD models including SVD++ blended with Restricted Boltzmann Machines. Using these methods they achieved a 10 percent increase in accuracy over Netflix’s existing algorithm. In this paper I explore the different facets of a successful recommender model. I also will explore a few of the more prominent SVD based models such as Iterative SVD, SVD++ and Regularized SVD. This paper is designed for a person with basic knowledge of decompositions and linear algebra and attempts to explain the workings of these algorithms in a way that most can understand.