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To efficiently resolve the problem that the new item is difficult to recommend in collaborative filtering algorithm. In this paper we propose a new method based item matrix partition.
为了有效地解决协同过滤算法中新项目难以推荐的问题，文中提出了一种对项目矩阵进行划分的方法。
In the third part we introduce the basal suppose of the Markov chains forecasting, including the states partition and the confirmation of the transferring matrix.
第三部分，对企业的财务报表应用马氏过程进行预测的基本假设，包括状态的划分，转移矩阵的确定。
Based on this point, this paper proposes a new task partition scheme for matrix QR decomposition, and a new coarse granularity parallel algorithm is given.
基于这一点，本文对矩阵的QR分解提出了一种新的任务划分策略，并由此得到了它的一种粗粒度并行算法。
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