让推荐系统读懂用户评论情绪，提升精准度和解释性。
Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective
- 用NLP提取评论情感，融合到推荐模型中
- 提升推荐准确率，增强结果可解释性
- 适合做智能推荐、对话系统的研究者
电商平台产生大量用户反馈，包括评分、文字评论和留言。然而，大多数推荐系统仅依赖数值评分，忽略了自由文本中的细微观点。本文从自然语言处理视角综述2023至2025年初的情感感知推荐系统进展，强调将情感分析融入电商推荐可提升预测准确性和可解释性。调研将近年工作分为四类：结合情感嵌入与用户-物品交互的深度学习分类器，用于精细特征提取的Transformer方法，传播情感信号的图神经网络，以及实时响应用户反馈的对话式推荐系统。文章总结模型架构，展示情感如何贯穿推荐流程并影响对话建议。关键挑战包括处理噪声或讽刺性文本、动态用户偏好建模及偏见缓解。最后，指出研究空白并提出构建更智能、公平、以用户为中心推荐工具的发展路线。
原文摘要 · Abstract (English)
E-commerce platforms generate vast volumes of user feedback, such as star ratings, written reviews, and comments. However, most recommendation engines rely primarily on numerical scores, often overlooking the nuanced opinions embedded in free text. This paper comprehensively reviews sentiment-aware recommendation systems from a natural language processing perspective, covering advancements from 2023 to early 2025. It highlights the benefits of integrating sentiment analysis into e-commerce recommenders to enhance prediction accuracy and explainability through detailed opinion extraction. Our survey categorizes recent work into four main approaches: deep learning classifiers that combine sentiment embeddings with user item interactions, transformer based methods for nuanced feature extraction, graph neural networks that propagate sentiment signals, and conversational recommenders that adapt in real time to user feedback. We summarize model architectures and demonstrate how sentiment flows through recommendation pipelines, impacting dialogue-based suggestions. Key challenges include handling noisy or sarcastic text, dynamic user preferences, and bias mitigation. Finally, we outline research gaps and provide a roadmap for developing smarter, fairer, and more user-centric recommendation tools.
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