Recommendation systems are designed to provide personalized predictions for items that are most appealing to individual customers. Among various types of recommendation algorithms, k-nearest neighbor based collaborative filtering algorithm attracts tremendous attention and are widely used in practice. However, the k-nearest neighbor scheme can only capture the local relationship among users and the uniform neighborhood size is also not suitable to represent the underlying data structure. In this paper, we leverage emerging graph signal processing (GSP) theory to construct sparse yet high quality graph to enhance the solution quality and efficiency of collaborative filtering algorithm. Experimental results show that our method outperforms k-NN based collaborative filtering algorithm by a large margin on the benchmark data set.

翻译：推荐系统旨在为个体用户提供最吸引其兴趣的个性化项目预测。在各类推荐算法中，基于k近邻的协同过滤算法因其显著效果而备受关注，并在实际应用中得到广泛采用。然而，k近邻方法仅能捕捉用户间的局部关系，且统一的邻域规模难以准确反映底层数据结构。本文利用新兴的图信号处理理论构建稀疏而高质量的图，以提升协同过滤算法的解质量与效率。实验结果表明，在基准数据集上，本方法在性能上大幅优于基于k近邻的协同过滤算法。