用用户偏好精准排序与总结评论，提升购物决策效率。
Improving User Experience with Personalized Review Ranking and Summarization
- 基于用户历史评论构建个性化的方面与情感偏好模型。
- 在亚马逊电子商品数据集上优于五种传统排序方法，用户满意度提升显著。
- 适合关注个性化推荐与信息过载问题的研究者与产品经理。
在线消费者评论是电商中的重要决策支持资源，但评论数量激增导致信息过载，难以匹配用户个体偏好。现有评论排序方法多依赖平均评分、有用性投票或发布时间等聚合信号，无法反映用户特定兴趣。本文提出一种个性化评论排序与摘要框架，融合用户偏好建模、混合情感估计、方面级评论匹配及大语言模型（LLM）摘要生成。首先从历史评论中提取方面级偏好与情感信号，再结合用户选定的产品方面和输入的评论内容构建个性化用户画像。通过对比该画像与评论级别的方面与情感表征，对候选评论进行排序，并对排名靠前的评论进行摘要，提供契合用户偏好的简洁信息。在亚马逊移动电子产品评论数据集上进行了评估，并开展涉及70名参与者的结构化用户研究。结果表明，所提方法在排序性能上优于随机排序、评分排序、有用性投票排序、时间排序和语义相似度排序。用户研究进一步显示，其在满意度、感知相关性、决策信心、信息查找便捷性和阅读效率方面均有提升。研究证明，结合方面级个性化、情感感知排序与LLM摘要，可有效缓解评论过载，支持更高效的以用户为中心的决策。
原文摘要 · Abstract (English)
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.
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