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中文摘要：
数据稀疏性是协同过滤系统面临的一个巨大挑战。本文提出了一种新的推荐算法——基于矩阵划分和兴趣方差的协同过滤算法。该算法采用矩阵分块的思想来缩小最近邻搜索的范围。矩阵分块时，采用聚类的方法，大大降低了矩阵的维度和稀疏等级。同时引入兴趣方差的概念，提高了计算最近邻的准确度。实验证明，本文提出的过滤算法在预测精度上较传统的推荐算法有很大的提高。
英文摘要：
Data sparseness is a serious problem in collaborative filtering system. In this paper, a new recommendation algorithm is presented, that is, a collaborative filtering algorithm based on Matrix Partition and Interest Variance. h partitions the huge matrix into some sub- matrixes in order to reduce the scale of searching nearest neighbors. In the course of partitioning the matrix, a clustering approach is applied to divide the sub - groups. Moreover, the concept of interest variance is adopted to improve the veracity of searching nearest neighbors. It proves that this method can obtain a better predictive precision, compared with traditional recommendation algorithm.
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