基于联合聚类和矩阵分解的协同过滤算法研究

提出了基于联合聚类和带正则化的迭代最小二乘法的协同过滤算法。该算法对原始矩阵进行用户-项目两个维度的联合聚类生成若干子矩阵，子矩阵的规模远小于原始评分矩阵，可有效降低预测阶段计算量，而且也缓解了数据稀疏性问题。在子矩阵中通过对传统的矩阵分解进行正则化约束来防止模型过拟合现象，并采用迭代最小二乘法进行训练分解模型，可有效缓解可扩展性。实验表明，该方法具有高效性。

Collaborative filtering algorithm based on co-clustering and matrix decomposition

This paper proposes a collaborative filtering algorithm based on co-clustering and alternating-least-squares with weighted-regularization .The algorithm divides the original matrix into several sub-matrix,and the sub-matrix is much smaller than the size of the original scoring matrix , which not only reduces the amount of computation , but also alleviates the problem of data sparsity .In the sub-matrix by using regularization constraint to prevent model from over fitting and by using least-squares method to train decomposition model ,the scalability can be effectively alleviated .The experiments show that this method is efficient .