基于边界矩阵低阶近似和近邻模型的协同过滤算法

为解决矩阵分解应用到协同过滤算法的局限性和准确率等问题，提出基于边界矩阵低阶近似（BMA）和近邻模型的协同过滤算法（BMAN-CF）来提高物品评分预测的准确率。首先，引入BMA的矩阵分解算法，挖掘子矩阵的隐含特征信息，提高近邻集合查找的准确率；然后，根据传统基于用户和基于物品的协同过滤算法分别预测出目标用户对目标物品的评分，利用平衡因子和控制因子动态平衡两个预测结果，得到目标用户对物品的评分；最后，利用MapReduce计算框架的特点，对数据进行分块，将该算法在Hadoop环境下并行化。实验结果表明，BMAN-CF比其他矩阵分解算法有更高的评分预测准确率，且加速比实验验证了该算法具有较好的可扩展性。

Collaborative filtering algorithm based on bounded matrix low rank approximation and nearest neighbor model

To solve the limitation and accuracy of matrix decomposition in Collaborative Filtering (CF) algorithm, a Collaborative Filtering algorithm based on Bounded Matrix low rank Approximation (BMA) and Nearest neighbor model (BMAN-CF) was proposed to improve the accuracy of item scoring prediction. Firstly, the matrix factorization algorithm of BMA was introduced to extract the implicit feature information of sub-matrix and improve the accuracy of neighborhood set search. Then, the target users' scores on target items were respectively predicted according to the traditional user-based and item-based collaborative filtering algorithms. And the equilibrium factor and control factor were used to dynamically balance the two prediction results, the target users' scores of items were obtained. Finally, the data was partitioned, and the proposed algorithm was parallelized in Hadoop environment by using the characteristics of MapReduce computing framework. The experimental results show that, the BMAN-CF has higher rating prediction accuracy than other matrix factorization algorithms, and the speedup experiment shows that the proposed parallelized algorithm has better scalability.