# Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

## TL;DR

This paper introduces a Bayesian model to quantify user aspect biases in ordinal ratings, improving the estimation of true item quality by accounting for individual biases and aspect dependencies.

## Contribution

It presents a novel probabilistic framework that models multi-aspect ordinal ratings with latent Gaussian structures and Bayesian inference to uncover user biases and item quality.

## Key findings

- Effective in predicting ratings on real datasets
- Learns explainable user biases
- Enhances reliability of product quality estimation

## Abstract

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.