Rating Prediction via Graph Signal Processing.

This paper develops new designs for recommendation systems inspired by recent advances in graph signal processing. Recommendation systems aim to predict unknown ratings by exploiting the information revealed in a subset of user-item observed ratings. Leveraging the notions of graph frequency and graph filters, we demonstrate that classical collaborative filtering methods, such as k -nearest neighb...