{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T15:53:08Z","timestamp":1786031588315,"version":"3.56.0"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>The rise of Large Language Models (LLMs), such as LLaMA and ChatGPT, has opened new opportunities for enhancing recommender systems through improved explainability. This paper provides a systematic literature review focused on leveraging LLMs to generate explanations for recommendations\u2014a critical aspect for fostering transparency and user trust. We conducted a comprehensive search within the ACM Guide to Computing Literature, covering publications from the launch of ChatGPT (November 2022) to the present (November 2024). Our search yielded 232 articles, but after applying inclusion criteria, only six were identified as directly addressing the use of LLMs in explaining recommendations. This scarcity highlights that, despite the rise of LLMs, their application in explainable recommender systems is still in an early stage. We analyze these select studies to understand current methodologies, identify challenges, and suggest directions for future research. Our findings underscore the potential of LLMs improving explanations of recommender systems and encourage the development of more transparent and user-centric recommendation explanation solutions.<\/jats:p>","DOI":"10.3389\/fdata.2024.1505284","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T06:49:12Z","timestamp":1737960552000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["On explaining recommendations with Large Language Models: a review"],"prefix":"10.3389","volume":"7","author":[{"given":"Alan","family":"Said","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-90403-0_2","article-title":"\u201cTransparency in fair machine learning: the case of explainable recommender systems,\u201d","volume-title":"Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent","author":"Abdollahi","year":"2018"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3629170","article-title":"Exploring the landscape of recommender systems evaluation: practices and perspectives","volume":"2","author":"Bauer","year":"2024","journal-title":"ACM Trans. Recommender Syst"},{"key":"B3","first-page":"1877","article-title":"\u201cLanguage models are few-shot learners,\u201d","volume-title":"Advances in Neural Information Processing Systems","author":"Brown","year":"2020"},{"key":"B4","first-page":"1","article-title":"\u201cUser attitudes towards algorithmic opacity and transparency in online reviewing platforms,\u201d","volume-title":"Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI'19","author":"Eslami","year":"2019"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3652891","article-title":"A survey on trustworthy recommender systems","volume":"3","author":"Ge","year":"2024","journal-title":"ACM Trans. Recomm. Syst"},{"key":"B6","first-page":"2786","article-title":"\u201cTowards explainable conversational recommender systems,\u201d","volume-title":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '23","author":"Guo","year":"2023"},{"key":"B7","doi-asserted-by":"crossref","first-page":"8142","DOI":"10.18653\/v1\/2020.emnlp-main.654","article-title":"\u201cINSPIRED: toward sociable recommendation dialog systems,\u201d","volume-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","author":"Hayati","year":"2020"},{"key":"B8","first-page":"256","article-title":"\u201cExplanations in open user models for personalized information exploration,\u201d","volume-title":"Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, UMAP Adjunct '24","author":"Hendrawan","year":"2024"},{"key":"B9","article-title":"Mistral 7b","author":"Jiang","year":"2023","journal-title":"arXiv:2310.06825"},{"key":"B10","author":"Kitchenham","year":"2007","journal-title":"Guidelines for performing systematic literature reviews in software engineering"},{"key":"B11","first-page":"9748","article-title":"\u201cTowards deep conversational recommendations,\u201d","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18","author":"Li","year":"2018"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3565480","article-title":"User perception of recommendation explanation: are your explanations what users need?","volume":"48","author":"Lu","year":"2023","journal-title":"ACM Trans. Inf. Syst"},{"key":"B13","first-page":"276","article-title":"\u201cLLM-generated explanations for recommender systems,\u201d","volume-title":"Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, UMAP Adjunct '24","author":"Lubos","year":"2024"},{"key":"B14","first-page":"4768","article-title":"\u201cA unified approach to interpreting model predictions,\u201d","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS'17","author":"Lundberg","year":"2017"},{"key":"B15","first-page":"16","article-title":"\u201cA user preference and intent extraction framework for explainable conversational recommender systems,\u201d","volume-title":"Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems, EICS '23 Companion","author":"Park","year":"2023"},{"key":"B16","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1145\/3640457.3688137","article-title":"\u201cInstructing and prompting large language models for explainable cross-domain recommendations,\u201d","volume-title":"Proceedings of the 18th ACM Conference on Recommender Systems, RecSys '24","author":"Petruzzelli","year":"2024"},{"key":"B17","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI blog"},{"key":"B18","first-page":"5485","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"140","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res"},{"key":"B19","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1145\/3372923.3404797","article-title":"\u201cPersonalizing information exploration with an open user model,\u201d","volume-title":"Proceedings of the 31st ACM Conference on Hypertext and Social Media, HT '20","author":"Rahdari","year":"2020"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1145\/2939672.2939778","author":"Ribeiro","year":"2016"},{"key":"B21","doi-asserted-by":"crossref","DOI":"10.1145\/3640543.3645171","article-title":"\u201cLeveraging ChatGPT for automated human-centered explanations in recommender systems,\u201d","volume-title":"Proceedings of the 29th ACM Conference on Intelligent User Interfaces., IUI '24","author":"Silva","year":"2024"},{"key":"B22","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1007\/978-1-0716-2197-4_19","article-title":"\u201cBeyond explaining single item recommendations,\u201d","volume-title":"Recommender Systems Handbook","author":"Tintarev","year":"2022"},{"key":"B23","article-title":"LLaMA: open and efficient foundation language models","author":"Touvron","year":"2023","journal-title":"arXiv:2302.13971"}],"container-title":["Frontiers in Big Data"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2024.1505284\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T06:49:22Z","timestamp":1737960562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2024.1505284\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,27]]},"references-count":23,"alternative-id":["10.3389\/fdata.2024.1505284"],"URL":"https:\/\/doi.org\/10.3389\/fdata.2024.1505284","relation":{},"ISSN":["2624-909X"],"issn-type":[{"value":"2624-909X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,27]]},"article-number":"1505284"}}