一种改进的基于流形对齐的协同过滤算法

An Improved Collaborative Filtering Algorithm Based on Manifold Alignments

基于流形对齐的协同过滤算法是通过用户间的相似性来计算重构矩阵,所以相似性计算的不准确往往会导致权值矩阵不准确,使得推荐质量下降.文中对基于流形对齐的协同过滤算法进行改进,采用数据集的拓扑结构矩阵和几何结构矩阵线性组合的方法来计算权值矩阵,有效去除相似性误差对推荐质量的影响.实验结果表明,改进后的算法与原算法相比有较好的推荐质量.

Collaborative filtering by the learning manifold alignments provides a new way for cross system personalization. It uses similarity between users to compute reconstruction weights. However, inaccurate similarity often leads to inaccurate weights and poor recommendation quality. By combining the topology and geometry structures of data set to calculate weight matrix, an algorithm based on manifold alignments is proposed. The proposed improved collaborative filtering algorithm removes the effect of similarity error on recommendation quality effectively. The experimental result indicates that the improved algorithm has better recommendation quality than that of the original algorithm.