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Beyond numerical ratings, textual reviews provide insights into users\u2019 fine-grained preferences and item features. Analyzing these reviews is crucial for enhancing the performance and explainability of personalized recommendation results. In this article, 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.<\/jats:p>","DOI":"10.1145\/3742421","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T04:54:49Z","timestamp":1748580889000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":44,"title":["Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3275-3158","authenticated-orcid":false,"given":"Emrul","family":"Hasan","sequence":"first","affiliation":[{"name":"Department of Copmuter Science, Toronto Metropolitan University","place":["Toronto, Canada"]},{"name":"AI Engineering, Vector Institute","place":["Toronto, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8896-7077","authenticated-orcid":false,"given":"Mizanur","family":"Rahman","sequence":"additional","affiliation":[{"name":"School of Information Technology, York University - Keele Campus","place":["Toronto, Canada"]},{"name":"Royal Bank of Canada","place":["Toronto, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0013-3439","authenticated-orcid":false,"given":"Chen","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Toronto Metropolitan University","place":["Toronto, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1292-1491","authenticated-orcid":false,"given":"Jimmy Xiangji","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information Technology, York University - Keele Campus","place":["Toronto, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1061-5845","authenticated-orcid":false,"given":"Shaina","family":"Raza","sequence":"additional","affiliation":[{"name":"AI Engineering, Vector Institute","place":["Toronto, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,5]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"769","volume-title":"Proceedings of the Recommender Systems Handbook","author":"Adomavicius Gediminas","year":"2010","unstructured":"Gediminas Adomavicius, Nikos Manouselis, and YoungOk Kwon. 2010. 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