Rating prediction based on the graph Fourier basis and PSD estimation from the perspective of graph signal reconstruction

Abstract

Accurately and quickly predicting unknown ratings from fewer known ratings has always been a challenging problem for recommender systems. Inspired by emerging graph signal processing techniques, this paper proposes a rating prediction method via a graph signal reconstruction technique. The most novel point is that the proposed method does not need to construct (or learn) the graph structures in advance and then estimate the power spectral density (PSD), but directly estimates the graph Fourier basis and the PSD from data. Furthermore, we present an approximate solution strategy to significantly reduce the computational complexity of the reconstructed model. Finally, we conduct experiments with two public databases to test the proposed method and compare it with existing methods. The experiments give an encouraging result in both prediction accuracy and efficiency.