Recommender systems play a pivotal role in helping users navigate an overwhelming selection of products and services. On online platforms, users have the opportunity to share feedback in various modes, including numerical ratings, textual reviews, and likes/dislikes. Traditional recommendation systems rely on users explicit ratings or implicit interactions (e.g. likes, clicks, shares, saves) to learn user preferences and item characteristics. Beyond these numerical ratings, textual reviews provide insights into users fine-grained preferences and item features. Analyzing these reviews is crucial for enhancing the performance and interpretability of personalized recommendation results. In recent years, review-based recommender systems have emerged as a significant sub-field in this domain. In this paper, we provide a comprehensive overview of the developments 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 present a categorization of these systems and summarize the state-of-the-art methods, analyzing their unique features, effectiveness, and limitations. Finally, we propose potential directions for future research, including the integration of multimodal data, multi-criteria rating information, and ethical considerations.

翻译：推荐系统在帮助用户应对海量商品与服务选择中发挥着关键作用。在线平台上，用户可通过多种方式分享反馈，包括数值评分、文本评论及点赞/不赞。传统推荐系统依赖用户的显式评分或隐式交互（如点赞、点击、分享、收藏）来学习用户偏好与物品特征。除数值评分外，文本评论能揭示用户细粒度偏好与物品属性特征，分析这些评论对于提升个性化推荐结果的性能与可解释性至关重要。近年来，基于评论的推荐系统已成为该领域的重要分支。本文系统梳理了基于评论推荐系统的最新研究进展，重点阐释评论文本在推荐系统中的重要性，以及从评论中提取特征并将其与评分信息融合所面临的挑战。具体而言，我们提出了这些系统的分类框架，总结了当前最优方法，分析了其独特优势、有效性及局限性。最后，我们展望了未来研究方向，包括多模态数据融合、多准则评分信息整合以及伦理考量等。