一种结合用户和项目聚类的协同过滤算法

An improved collaborative filtering algorithm based on user clustering and item clustering

针对协同过滤算法中评分矩阵稀疏性问题和可拓展性问题,提出一种改进的协同过滤算法,以望提高推荐效果和计算性能。该算法结合用户聚类和项目聚类,将最近邻搜索空间缩小到对应的聚类中,重新构建了相似度计算和评分预测方法。在Movie Lens数据集上的实验结果显示,改进后的算法相较于传统协同过滤算法在预测准确度上有所提高。

To solve the problem of data sparseness and scalability in collaborative filtering algorithm,this paper proposes an improved collaborative filtering algorithm for the purpose of improving the recommendation quality and computing performance.This algorithm rebuilds the calculation methods of similarities and prediction scores on the basis of combining user clustering and item clustering as a result of the nearest neighbor space reduction.The experimental results on the MovieLens dataset show that the improved algorithm can improve the prediction accuracy of recommendation system compared to the traditional collaborative filtering algorithm.