Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

Opinion of users expressed in the form of observed ratings can influence an individual’s view of an item. However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings the importance users place on different aspects of an item. In this paper, we propose a probabilistic modeling of the observed aspect ratings to infer (i) each user’s aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with recently developed Pólya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model over state-of-theart baselines and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.