  User opinions expressed in the form of 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
different users place on different aspects of an item. 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 P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully
Bayesian inference. On two real world datasets, we demonstrate the predictive
ability of our model and its effectiveness in learning explainable user biases
to provide insights towards a more reliable product quality estimation.
