基于Bregman联合聚类与加权矩阵分解的 融合推荐算法

针对当前大数据背景下推荐系统中所存在推荐效率低下、扩展性差、推荐质量不高等问题，本文提出一种基于Bregman联合聚类与加权矩阵分解的融合推荐算法（CO-CWMA）。首先，通过Bregman联合聚类挖掘出多样、不同层次的低秩评分子矩阵，组合不同约束与距离的聚类结果训练得到子模型，进而在各个模型的子矩阵上并发地进行矩阵分解，最后将各个子模型进行均值融合，提高推荐质量、效率与扩展性。在矩阵分解阶段采用SVD++算法，基于每个子矩阵中的评分分布计算加权策略，给予高频评分较大权值，在梯度下降阶段利用学习率函数控制学习率的更新。实验结果表明，该算法与三种基线算法相比在均方根误差（RMSE）与平均绝对误差（MAE）上均有明显降低，即推荐质量有较大提升。

A Fusion Recommendation Algorithm based on Bregman Co-clustering and weighted Matrix Approximation

Aiming at the problems of low recommendation efficiency, recommendation quality and poor expansibility in the recommendation system, a fusion recommendation algorithm based on Bregman co-clustering and weighted matrix approximation (CO-CWMA) was proposed. Firstly, the Bregman co-clustering was used to mine the low-rank sub-matrices of different levels, clustering results of different constraints and distances were combined to train the sub-models, then the matrix approximation was performed concurrently on the sub-matrices of each model. Finally, each sub-model performed mean fusion to improve recommendation quality, efficiency and scalability. SVD++ algorithm was used in the matrix approximation stage, weighting strategy was calculated based on the score distribution, and high-frequency score got a larger weight. Learning rate function was used to control the learning rate during the gradient descent phase. Experimental results show that the proposed algorithm has a significant reduction in root mean square error (RMSE) and mean absolute error (MAE) compared with the three baseline algorithms, that is, recommended quality is greatly improved.