Recommender systems play a pivotal role in helping users navigate a vast selection of products and services. On online platforms, users have the opportunity to share feedback in various modes, such as numerical ratings, textual reviews, and likes/dislikes. Traditional recommendation systems rely on users’ explicit ratings or implicit interactions (e.g., likes, clicks, shares, and saves) to learn user preferences and item characteristics. Beyond numerical ratings, textual reviews provide insights into users’ fine-grained preferences and item features. Analyzing these reviews is crucial for enhancing the performance and explainability of personalized recommendation results. In this paper, we provide a comprehensive overview of the development in review-based recommender systems over recent years, highlighting the importance of reviews in recommender systems, as well as the challenges associated with extracting features from reviews and integrating them into ratings. Specifically, we introduce a classification scheme in terms of both the integration of reviews into recommendation systems and the technical methodology. Additionally, we summarize the state-of-the-art methods, analyzing their unique features, effectiveness, and limitations. The study also presents the various evaluation metrics, comparative analysis, datasets, and real-world applications of review-based recommendation systems. Finally, we propose potential directions for future research, including multi-modal data integration, multi-criteria rating information, and ethical considerations.

中文翻译：

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基于评论的推荐系统：方法、挑战和未来前景调查

推荐系统在帮助用户浏览大量产品和服务方面发挥着关键作用。在在线平台上，用户有机会以各种方式分享反馈，例如数字评分、文本评论和喜欢/不喜欢。传统的推荐系统依靠用户的显式评分或隐互（例如，点赞、点击、分享和保存）来了解用户偏好和项目特征。除了数字评分之外，文本评论还可以深入了解用户的细粒度偏好和项目功能。分析这些评论对于提高个性化推荐结果的性能和可解释性至关重要。在本文中，我们全面概述了近年来基于评论的推荐系统的发展，强调了评论在推荐系统中的重要性，以及与从评论中提取特征并将其集成到评级中相关的挑战。具体来说，我们引入了一种分类方案，既考虑了将综述整合到推荐系统中，也介绍了技术方法。此外，我们总结了最先进的方法，分析了它们的独特之处、有效性和局限性。该研究还介绍了基于评论的推荐系统的各种评估指标、比较分析、数据集和实际应用。最后，我们提出了未来研究的潜在方向，包括多模态数据集成、多标准评级信息和伦理考虑。