BC-AW协同过滤推荐算法研究

Study on BC-AW Collaborative Filtering Recommendation Algorithm

针对现有协同过滤算法普遍存在数据稀疏、可扩展性低、计算量大的缺点,提出一种基于BC-AW的协同过滤推荐算法,引入联合聚类(Block Clust,BC)和正则化迭代最小二乘法(Alternating least squares with Weighted regularization,AW),首先对原评分矩阵进行用户—项目双维度的联合聚类,接着产生具有相同模式评分块的多个子矩阵,通过分析得出这些子矩阵规模远小于原评分矩阵,从而有效降低预测阶段的计算量.然后分别对每个子矩阵应用正则化迭代最小二乘法来预测子矩阵的未知评分,进而实现推荐.经仿真实验表明,本文算法与传统的协同过滤算法比较,能有效改善稀疏性、可扩展性和计算量的问题.

Aiming at the weaknesses of sparse data, low scalability and large computing existing in the current collaborative filtering algorithm, a Block Clust-Alternating least squares with Weighted regularization（BC-AW）collaborative filtering recommendation algorithm is proposed. Firstly, the user and the item of the original scoring matrix are jointly clustered and several submatrixes with the same scoring mode are generated. According to the research, the scale of these submatrixes is far less than the original scoring matrix which effectively decreases the computational complexity in the prediction process. Then, the regularized iterative least-square method is applied to each submatrix to predict its score. Hence recommendation is realized. The simulation results reveal that the proposed algorithm can effectively improve sparsity, expand scalability, and reduce computing compared with the traditional one.