让评论总结懂用户喜好，智能推荐更贴心。
SUMFORU: An LLM-Based Review Summarization Framework for Personalized Purchase Decision Support
- 用用户画像引导大模型生成个性化摘要
- 在亚马逊数据上实现高一致性和偏好匹配
- 适合想高效选商品的消费者和电商研究者
在线产品评论包含丰富但嘈杂的信息，容易让用户信息过载，影响决策。现有基于大模型的摘要工具普遍通用化，未能考虑个体偏好，实用性受限。本文提出SUMFORU，一个可调控的评论摘要框架，通过显式用户画像对齐输出，支持个性化购买决策。方法结合高质量的Amazon 2023 Review Dataset数据管道与两阶段对齐机制：(1) 采用非对称知识蒸馏进行面向人物画像的有监督微调（SFT）；(2) 利用偏好估计器进行基于AI反馈的强化学习（RLAIF），捕捉细粒度、与人物画像相关的信号。我们在规则基、大模型基和人类中心指标上评估，结果表明在一致性、事实性与偏好对齐方面均有持续提升。该框架在所有评测场景中表现最优，并能有效泛化至未见商品类别。研究证明了可调控多视角对齐在下一代个性化决策支持系统中的潜力。
原文摘要 · Abstract (English)
Online product reviews contain rich but noisy signals that overwhelm users and hinder effective decision-making. Existing LLM-based summarizers remain generic and fail to account for individual preferences, limiting their practical utility. We propose SUMFORU, a steerable review summarization framework that aligns outputs with explicit user personas to support personalized purchase decisions. Our approach integrates a high-quality data pipeline built from the Amazon 2023 Review Dataset with a two-stage alignment procedure: (1) persona-aware Supervised Fine-Tuning (SFT) via asymmetric knowledge distillation, and (2) Reinforcement Learning with AI Feedback (RLAIF) using a preference estimator to capture fine-grained, persona-relevant signals. We evaluate the model across rule-based, LLM-based, and human-centered metrics, demonstrating consistent improvements in consistency, grounding, and preference alignment. Our framework achieves the highest performance across all evaluation settings and generalizes effectively to unseen product categories. Our results highlight the promise of steerable pluralistic alignment for building next-generation personalized decision-support systems.
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