PREFER: Personalized Review Summarization with Online Preference Learning

TL;DR

This paper introduces PREFER, an online learning system that creates personalized product review summaries by adapting to individual user preferences over time, enhancing relevance and user satisfaction.

Contribution

It presents a novel online preference learning framework for personalized review summarization that updates based on user feedback during interactions.

Findings

Online preference learning improves summary relevance to user interests.

The system maintains high summary quality while adapting to preferences.

Case study on Amazon Reviews'23 dataset demonstrates effectiveness.

Abstract

Product reviews significantly influence purchasing decisions on e-commerce platforms. However, the sheer volume of reviews can overwhelm users, obscuring the information most relevant to their specific needs. Current e-commerce summarization systems typically produce generic, static summaries that fail to account for the fact that (i) different users care about different product characteristics, and (ii) these preferences may evolve with interactions. To address the challenge of unknown latent preferences, we propose an online learning framework that generates personalized summaries for each user. Our system iteratively refines its understanding of user preferences by incorporating feedback directly from the generated summaries over time. We provide a case study using the Amazon Reviews'23 dataset, showing in controlled simulations that online preference learning improves alignment with target user interests while maintaining summary quality.