Online consumer reviews are important decision-support resources in e-commerce, yet the increasing volume of reviews often creates information overload and makes it difficult for users to identify content that matches their individual preferences. Existing review-ranking approaches commonly rely on aggregate signals such as star ratings, helpfulness votes, or recency, which may not reflect user-specific interests. This paper proposes a personalized review ranking and summarization framework that integrates user preference modeling, hybrid sentiment estimation, aspect-level review matching, and Large Language Model (LLM)-based summarization. The framework first extracts aspect-level preferences and sentiment signals from historical reviews. It then incorporates user-selected product aspects and written review input to build a personalized user profile. Candidate reviews are ranked by comparing this profile with review-level aspect and sentiment representations. The top-ranked reviews are then summarized to provide concise, preference-aligned information. The proposed method was evaluated using an Amazon Mobile Electronics review dataset and a structured user study involving 70 participants across common consumer electronics categories. Results show that the proposed ranking method outperformed random ordering, star-rating-based ranking, helpfulness-vote ranking, recency-based ranking, and semantic-similarity-based ranking. User-study results further indicate improvements in satisfaction, perceived relevance, decision-making confidence, ease of finding information, and reading efficiency. The findings suggest that combining aspect-level personalization, sentiment-aware ranking, and LLM-based summarization can reduce review overload and support more efficient user-centered decision-making.

翻译：在线消费者评论是电子商务中重要的决策支持资源，但日益增长的评论数量常导致信息过载，使用户难以识别符合个人偏好的内容。现有评论排名方法通常依赖星级评分、有用性投票或时效性等聚合信号，无法反映用户的个性化兴趣。本文提出一种融合用户偏好建模、混合情感估计、方面级评论匹配与大语言模型（LLM）摘要的个性化评论排名与摘要框架。该框架首先从历史评论中提取方面级偏好与情感信号，进而结合用户选择的产品属性与撰写的评论输入构建个性化用户画像，通过对比用户画像与评论的方面及情感表征对候选评论进行排序，最后将排名靠前的评论摘要生成简洁且符合偏好的信息。在亚马逊移动电子产品评论数据集及涵盖70名参与者的跨常见消费电子产品类别结构化用户研究中，实验结果显示所提排名方法优于随机排序、基于星级评分、有用性投票、时效性及语义相似度的排名方法。用户研究进一步表明该方法在满意度、感知相关性、决策信心、信息查找便捷性与阅读效率方面均有提升。研究表明，结合方面级个性化、情感感知排名与大语言模型摘要可有效缓解评论过载问题，支持更高效的用户中心化决策。