MapReduce implementation of Variational Bayesian Probabilistic Matrix Factorization algorithm
MapReduce implementation of Variational Bayesian Probabilistic Matrix Factorization algorithm
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DOI：
10.1109/bigdata.2013.6691747
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
N. C. Tewari;H. M. Koduvely;S. Guha;Arun Yadav;Gladbin David
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文献类型：
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作者：
N. C. Tewari;H. M. Koduvely;S. Guha;Arun Yadav;Gladbin David
本文介绍了一种基于MapReduce框架的变分贝叶斯矩阵分解（Variational Bayesian Matrix Factorization）协同过滤的可扩展实现。变分贝叶斯方法的优点是为后验分布提供了很好的近似解析解。由于后验分布中参数的独立性假设，变分方法也可能能够有效地并行化。虽然变分贝叶斯矩阵分解方法在协同过滤中显示出更精确的结果，但其尺度特性目前尚未得到研究。我们在CiteULike数据集上运行MapReduce实现，并显示我们的并行化方案实现了近似线性扩展。我们还将其性能与MapReduce实现的一种流行的矩阵分解算法ALSWR进行了比较，该算法来自开源机器学习库Mahout。
We introduce in this paper a scalable implementation of Variational Bayesian Matrix Factorization method for collaborative filtering using the MapReduce framework. Variational Bayesian methods have the advantage of providing good approximate analytical solutions for the posterior distribution. Due to the independence assumption about the parameters in the posterior distribution, variational methods are also likely to be able to parallelize efficiently. Though Variational Bayesian Matrix Factorization method has shown to produce more accurate results in collaborative filtering, its scaling properties have not studied so far. We ran our MapReduce implementation on the CiteULike data set and show that our parallelization scheme achieves approximately linear scaling. We also compare its performance with the MapReduce implementation of a popular matrix factorization algorithm, ALSWR, from the open source machine learning library Mahout.