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.

翻译：产品评论显著影响电商平台上的购买决策。然而，海量评论可能使用户不知所措，掩盖了与其特定需求最相关的信息。当前的电商摘要系统通常生成通用、静态的摘要，未能考虑以下两点：（i）不同用户关注不同的产品特征；（ii）这些偏好可能随交互而演变。为了应对未知潜在偏好的挑战，我们提出一个在线学习框架，为每位用户生成个性化摘要。我们的系统通过随时间直接吸收从已生成摘要中获取的反馈，迭代地优化对用户偏好的理解。我们利用Amazon Reviews'23数据集进行了案例研究，在受控模拟中表明，在线偏好学习在保持摘要质量的同时，提高了与目标用户兴趣的一致性。