根据用户反馈动态调整，生成更贴合个人偏好的商品评论摘要。
PREFER: Personalized Review Summarization with Online Preference Learning
- 通过在线学习实时捕捉用户偏好变化
- 在亚马逊评论数据集上验证，偏好匹配度提升显著
- 适合个性化推荐与智能客服场景
商品评论对电商平台的购买决策有重要影响，但评论数量庞大，容易让用户信息过载，难以获取自身关注的信息。现有总结系统多生成通用、静态的摘要，未能考虑用户关注点差异及偏好随交互动态演变的特点。为解决隐含偏好未知的问题，我们提出一种在线学习框架，通过持续接收用户对生成摘要的反馈，迭代优化对用户偏好的理解，实现个性化摘要生成。基于Amazon Reviews'23数据集的案例研究显示，在受控模拟中，该方法能有效提升摘要与目标用户兴趣的契合度，同时保持摘要质量。
原文摘要 · Abstract (English)
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.
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