A recommendation prediction method based on the estimation of PSD of sampled signals on graph

Abstract

In recommendation system, the rating prediction is a very challenging problem. In this paper, we model users (items) and their relationships as a weighted undirected graph, and propose a rating prediction model based on graph signal reconstruction. Different from the existing reconstruction model, the proposed model takes into account the frequency structure of the signal itself, and tends to protect the frequency contents with higher power spectral density (PSD) and impose large penalties on those with lower PSD when reconstructing a signal. Furthermore, based on the assumption of the stationarity of the graph signal, we propose a method to estimate the PSD of a stationarity stochastic graph signal directly from some sampled signals without its any other complete implementation. Related theoretical analysis is also given. Finally, a rating prediction algorithm based on the graph signal reconstruction is derived. Experimental results show that the proposed model leads a significant improvement of the predictive accuracy.