大多数评分预测研究都是基于缺失值是随机的假设。然而，实际的线上推荐系 统的评分矩阵的缺失数据都是非随机的。对缺失数据的错误假设会导致有偏差的参数估 计和预测。为了提高非随机缺失评分矩阵填补的准确度，文中深入分析了用户和物品的 评分矩阵的内在原理，提出了通过行或列变换将用户和物品的评分矩阵转变为等价的双 边块对角矩阵，再在不同的分区块中分别应用矩阵分解方法进行分解和评分预测的方 法，使得局部数据更新和分解成为现实。在公测数据集上的实验结果显示，文中方法可 以提高评分填补效果，有效地解决非随机评分缺失问题，从而提高推荐系统的预测准确 率。变换后的分块矩阵在分布式处理实验中也获得了较好的加速比，说明文中方法具有 较好的应用可扩展性。

Most score prediction studies are based on the assumption that the missing values are random． However， the missing data of the score matrix of the actual on-line recommendation system is non-random． Incorrect assumptions about the missing data can lead to biased parameter estimation and prediction． In order to improve the accuracy of non-random missing score matrix filling，the internal principle of user and item score matrix was analyzed in this paper． It presents a method to transform the score matrix of user and object into the equivalent bilateral block diagonal matrix by row or column transformation． Then the matrix decomposition method was applied to different blocks to decompose and predict the score，making local data update and decomposition become a reality． The experimental results on the public test dataset show that the proposed method can improve the score filling effect，solve the problem of non-random score missing effectively，and improve the prediction accuracy of the recommendation system． The improved block matrix also has a better speedup ratio in the distributed processing experiment，which shows that the proposed method has better scalability．