An Interpretable Preference Learning Model Admitting Dynamic and Context-Dependent Preferences

We introduce a novel Bayesian preference learning approach for online user rating scenarios. Leveraging user-generated textual reviews, our model automatically extracts feature-opinion-polarity triplets based on phrase-level sentiment analysis. They are summarized as positive and negative topics, providing a low-dimensional feature-based representation of items. Moreover, our approach captures dynamic and context-dependent user preferences by inferring latent motivations represented as discrete distributions on the set of items and contextual preference models. We apply the proposed model to real-world Amazon data concerning cell phones and accessories. Also, we demonstrate its scalability, interpretability, and superior predictive performance against four benchmark algorithms in the extensive computational experiment. Our research contributes to advancing preference learning in domains such as recommendation systems, decision-making, and marketing, offering both actionable insights and transparent recommendations.