Methods for large scale SVD with missing values
Methods for large scale SVD with missing values
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发表时间：
2007
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通讯作者：
Miklós Kurucz;A. Benczúr;Károly Csalogány
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作者：
Miklós Kurucz;A. Benczúr;Károly Csalogány
我们只根据评级矩阵的低秩近似值来比较排名。关键的困难在于矩阵中已知评分的稀疏性，这导致expactation最大化算法收敛非常缓慢。在之前的公开已知的尝试，这个问题的梯度提升方法证明是最成功的，尽管事实上，所得到的向量是非正交的，容易出现数值错误。我们系统地探讨了期望最大化方法的Lanczos算法和电源迭代的基础上，在本文中的新颖性是作为输入到下一次迭代的密集估计矩阵的有效处理。我们还比较了序列变换方法，以加快收敛速度。
We compare recommenders based solely on low rank approximations of the rating matrix. The key difficulty lies in the sparseness of the known ratings within the matrix that cause expactation maximization algorithms converge very slow. Among the prior publicly known attempts for this problem a gradient boosting approach proved most successful in spite of the fact that the resulting vectors are nonorthogonal and prone to numeric errors. We systematically explore expectation maximization methods based both on the Lanczos algorithm and power iteration; novel in this paper is the efficient handling of the dense estimate matrix used as input to a next iteration. We also compare sequence transformation methods to speed up convergence.