Applications of the conjugate gradient method for implicit feedback collaborative filtering
Applications of the conjugate gradient method for implicit feedback collaborative filtering
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DOI：
10.1145/2043932.2043987
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
G. Takács;I. Pilászy;D. Tikk
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文献类型：
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作者：
G. Takács;I. Pilászy;D. Tikk
解决加权岭回归（WRR）问题的需要出现在一些协同过滤（CF）算法。通常，没有足够的时间来计算WRR问题的精确解，或者不需要。共轭梯度（CG）方法是一种近似求解WRR问题的先进方法。在本文中，我们研究了CG方法在新的和现有的隐反馈CF模型中的应用。我们通过在Netflix数据集上的实验证明，CG可以成为训练隐式反馈CF模型的有效工具。
The need for solving weighted ridge regression (WRR) problems arises in a number of collaborative filtering (CF) algorithms. Often, there is not enough time to calculate the exact solution of the WRR problem, or it is not required. The conjugate gradient (CG) method is a state-of-the-art approach for the approximate solution of WRR problems. In this paper, we investigate some applications of the CG method for new and existing implicit feedback CF models. We demonstrate through experiments on the Netflix dataset that CG can be an efficient tool for training implicit feedback CF models.