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马尔可夫链蒙特卡罗的贝叶斯概率矩阵分解
Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo |
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| 课程网址: |
http://videolectures.net/icml08_salakhutdinov_bpm/
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| 主讲教师: |
Ruslan Salakhutdinov
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| 开课单位: |
卡内基梅隆大学
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| 开课时间: |
2008-08-07
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| 课程语种: |
英语
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| 中文简介: |
低秩矩阵近似方法提供了协作过滤的最简单和最有效的方法之一。这些模型通常通过查找模型参数的MAP估计来拟合数据，即使在非常大的数据集上也可以有效地执行该过程。但是，除非仔细调整正则化参数，否则这种方法容易过度拟合，因为它找到了参数的单点估计。在本文中，我们提出了概率矩阵分解（PMF）模型的完全贝叶斯处理，其中模型容量通过整合所有模型参数和超参数自动控制。我们展示了贝叶斯PMF模型可以使用马尔可夫链蒙特卡罗方法进行有效训练，方法是将它们应用于Netflix数据集，该数据集包含超过1亿的用户/电影评级。得到的模型比使用MAP估计训练的PMF模型实现了显着更高的预测精度。
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| 课程简介: |
Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP estimate of the model parameters, a procedure that can be efficiently performed even on very large datasets. However, unless the regularization parameters are tuned carefully, this approach is prone to overfitting because it finds a single point estimate of the parameters. In this paper we present a fully Bayesian treatment of the Probabilistic Matrix Factorization (PMF) model in which model capacity is controlled automatically by integrating over all model parameters and hyperparameters. We show that Bayesian PMF models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the Netflix dataset, which consists of over 100 million user/movie ratings. The resulting models achieve significantly higher prediction accuracy than PMF models trained using MAP estimation.
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| 关 键 词: |
低秩矩阵近似方法; 协作过滤; 概率矩阵分解
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-22：chenxin
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| 阅读次数: |
97
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