{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:39:38Z","timestamp":1780609178950,"version":"3.54.1"},"reference-count":178,"publisher":"Emerald","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,11]]},"abstract":"<jats:p>Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helpsendation systems. It also facilitates system design to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommers for better system debugging. In recent years, a large number of explainable recommendation approaches \u2013 especially model-based methods \u2013 have been proposed and applied in real-world systems.<\/jats:p>\n                  <jats:p>In this survey, we provide a comprehensive review for the explainable recommendation research. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation on three perspectives: 1) We provide a chronological research timeline of explainable recommendation, including user study approaches in the early years and more recent model-based approaches. 2) We provide a two-dimensional taxonomy to classify existing explainable recommendation research: one dimension is the information source (or display style) of the explanations, and the other dimension is the algorithmic mechanism to generate explainable recommendations. 3) We summarize how explainable recommendation applies to different recommendation tasks, such as product recommendation, social recommendation, and POI recommendation.<\/jats:p>\n                  <jats:p>We also devote a section to discuss the explanation perspectives in broader IR and AI\/ML research. We end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.<\/jats:p>","DOI":"10.1561\/1500000066","type":"journal-article","created":{"date-parts":[[2020,4,10]],"date-time":"2020-04-10T04:18:17Z","timestamp":1586492297000},"page":"1-101","source":"Crossref","is-referenced-by-count":651,"title":["Explainable Recommendation: A Survey and New Perspectives"],"prefix":"10.1108","volume":"14","author":[{"given":"Yongfeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Rutgers University, USA;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, China;"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2020,3,11]]},"reference":[{"key":"2026040314515859300_ref001","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1145\/2872518.2889405","volume-title":"Proceedings of the 25th International Conference Companion on World Wide Web.","author":"Abdollahi","year":"2016"},{"key":"2026040314515859300_ref002","first-page":"79","volume-title":"Proceedings of the 11th ACM Conference on Recommender Systems.","author":"Abdollahi","year":"2017"},{"key":"2026040314515859300_ref003","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/978-0-387-85820-3_7","volume-title":"Recommender Systems Handbook.","author":"Adomavicius","year":"2011"},{"key":"2026040314515859300_ref004","first-page":"487","volume-title":"Proceedings of the 20th VLDB Conference.","author":"Agarwal","year":"1994"},{"issue":"2","key":"2026040314515859300_ref005","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1145\/170036.170072","article-title":"Mining association rules between sets of items in large databases","volume":"22","author":"Agrawal","year":"1993","journal-title":"ACM SIGMOD Record."},{"issue":"9","key":"2026040314515859300_ref006","doi-asserted-by":"crossref","first-page":"137","DOI":"10.3390\/a11090137","article-title":"Learning heterogeneous knowledge base embeddings for explainable recommendation","volume":"11","author":"Ai","year":"2018","journal-title":"Algorithms."},{"issue":"1","key":"2026040314515859300_ref007","first-page":"4","article-title":"Explainable product search with a dynamic relation embedding model","volume":"38","author":"Ai","year":"2019","journal-title":"ACM Transactions on Information Systems (TOIS)."},{"key":"2026040314515859300_ref008","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/978-1-4899-7637-6_7","volume-title":"Recommender Systems Handbook.","author":"Amatriain","year":"2015"},{"issue":"1","key":"2026040314515859300_ref009","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1111\/j.1365-2575.2006.00208.x","article-title":"Cognitive biases and decision support systems development: A design science approach","volume":"16","author":"Arnott","year":"2006","journal-title":"Information Systems Journal."},{"issue":"3","key":"2026040314515859300_ref010","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1145\/245108.245124","article-title":"Fab: Content-based, collaborative recommendation","volume":"40","author":"Balabanovic","year":"1997","journal-title":"Communications of the ACM."},{"key":"2026040314515859300_ref011","volume-title":"Proceedings of the f2nd International ACM SIGIR Conference on Research and Development in Information Retrieval.","author":"Balog","year":"2019"},{"key":"2026040314515859300_ref012","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1145\/3209219.3209237","volume-title":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization.","author":"Baral","year":"2018"},{"key":"2026040314515859300_ref013","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1145\/3097983.3098170","volume-title":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.","author":"Bauman","year":"2017"},{"key":"2026040314515859300_ref014","first-page":"27","volume-title":"Computer Sciences Austin, University of Texas. 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