Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach
This addresses the issue of unreliable product quality estimation from biased user ratings, which is an incremental improvement in recommendation systems.
The paper tackles the problem of user biases obscuring true item quality in multi-aspect ratings by proposing a Bayesian model to infer user aspect biases and latent intrinsic quality, demonstrating predictive ability and explainable bias learning on two real-world datasets.
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 Pólya-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.