A Mixture-of-Gaussians model for estimating the magic barrier of the recommender system

Abstract

The rating data collected by the recommender system usually contains noise due to external factors such as human uncertainty and inconsistency. Such noise, usually modeled by a normal distribution, leads to a magic barrier (MGBR) to the recommender system. However, existing MGBR estimation approaches require a user-specified standard deviation of noise, or make strong assumptions about true ratings, or need additional information from experts or users. In this paper, we propose a Mixture of Gaussians (MoG) model without user intervention to handle this issue. First, the user uncertainties are modeled using MoG, which is a universal approximator for any continuous distribution. Second, we employ the expectation–maximization algorithm to determine the parameters of user uncertainty. Finally, the MGBR is computed by Bayesian formula with the parameters. Experimental results on four well-known datasets show that the MGBRs estimated by the new model are close to the results of the state-of-the-art algorithms.