基于矩阵分解和聚类的协同过滤算法

基于矩阵分解和聚类提出一种协同过滤推荐算法. 先利用交替最小二乘（ALS)算法进行矩阵分解, 再利用改进的k-均值聚类算法弥补单一ALS算法在后期协同过滤阶段产生的大计算量问题, 解决了由于减小原始矩阵高维度、 高稀疏性带来的推荐准确度较低的问题, 极大提高了计算速度和推荐精度. 实验结果表明, 改进算法在推荐准确性上有明显提高.

Collaborative Filtering Algorithm Based onMatrix Decomposition and Clustering#br#

Based on the matrix decomposition and clustering, we proposed a collaborative filtering recommendation algorithm. The algorithm first used alternating least squares (ALS) algorithm to decompose matrix, and then used improved k-means clustering algorithm to compensate for large amount of calculation caused by single ALSalgorithm in the later stage of collaborative filtering. The problem that the recommendation accuracy was low due to the reduction of the high dimension and high sparsity of original matrix was solved, and the calculation speed and recommendation accuracy were greatly improved. Experimental results show that the improved algorithm significantly improves the recommendation accuracy.