PREFER: Personalized Review Summarization with Online Preference Learning
For e-commerce platforms, this addresses the need to help users quickly find relevant review information, though the results are based on controlled simulations rather than real user studies.
The paper tackles the problem of generic product review summaries that ignore individual user preferences. It proposes an online learning framework that personalizes summaries by iteratively refining user preference models from feedback, showing improved alignment with target interests in simulations on the Amazon Reviews'23 dataset.
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.