{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:19:43Z","timestamp":1777594783437,"version":"3.51.4"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JZ2024HGTB0222"],"award-info":[{"award-number":["JZ2024HGTB0222"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72071061"],"award-info":[{"award-number":["72071061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72201086"],"award-info":[{"award-number":["72201086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.ins.2026.123525","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T23:46:13Z","timestamp":1776383173000},"page":"123525","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Decomposing preferences with a large language model for accurate and interpretable rating prediction"],"prefix":"10.1016","volume":"748","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9912-3443","authenticated-orcid":false,"given":"Ying","family":"Yang","sequence":"first","affiliation":[]},{"given":"Ying-Ying","family":"Guo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4689-0099","authenticated-orcid":false,"given":"Gang","family":"Ren","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.ins.2026.123525_bib0005","first-page":"145","article-title":"Recommender systems: trends and frontiers","volume":"43","author":"Jannach","year":"2022","journal-title":"AI Mag."},{"key":"10.1016\/j.ins.2026.123525_bib0010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/1500000066","article-title":"Explainable recommendation: a survey and new perspectives","volume":"14","author":"Zhang","year":"2020","journal-title":"Found. Trends Inf. Retr."},{"key":"10.1016\/j.ins.2026.123525_bib0015","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"key":"10.1016\/j.ins.2026.123525_bib0020","series-title":"Proceedings of the Eleventh ACM Conference on Recommender Systems","first-page":"288","article-title":"Transnets: learning to transform for recommendation","author":"Catherine","year":"2017"},{"key":"10.1016\/j.ins.2026.123525_bib0025","series-title":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","first-page":"3748","article-title":"A3ncf: an adaptive aspect attention model for rating prediction","author":"Cheng","year":"2018"},{"key":"10.1016\/j.ins.2026.123525_bib0030","series-title":"Proceedings of the 26th International Conference on World Wide Web","first-page":"173","article-title":"Neural collaborative filtering","author":"He","year":"2017"},{"key":"10.1016\/j.ins.2026.123525_bib0035","doi-asserted-by":"crossref","first-page":"114540","DOI":"10.1109\/ACCESS.2022.3217911","article-title":"Neural collaborative filtering for Chinese movies based on aspect-aware implicit interactions","volume":"10","author":"Deng","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.ins.2026.123525_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106687","article-title":"Accurate and explainable recommendation via hierarchical attention network oriented towards crowd intelligence","volume":"213","author":"Yang","year":"2021","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.ins.2026.123525_bib0045","doi-asserted-by":"crossref","first-page":"59:1","DOI":"10.1145\/3627158","article-title":"Contrastive self-supervised learning in recommender systems: a survey","volume":"42","author":"Jing","year":"2024","journal-title":"ACM Trans. Inf. Syst."},{"key":"10.1016\/j.ins.2026.123525_bib0050","series-title":"Proceedings of the 16th ACM Conference on Recommender Systems","first-page":"299","article-title":"Recommendation as language processing (rlp): a unified pretrain, personalized prompt & predict paradigm (p5)","author":"Geng","year":"2022"},{"key":"10.1016\/j.ins.2026.123525_bib0055","series-title":"Findings of the Association for Computational Linguistics: EMNLP 2024","first-page":"391","article-title":"Xrec: large language models for explainable recommendation","author":"Ma","year":"2024"},{"key":"10.1016\/j.ins.2026.123525_bib0060","doi-asserted-by":"crossref","first-page":"103:1","DOI":"10.1145\/3580488","article-title":"Personalized prompt learning for explainable recommendation","volume":"41","author":"Li","year":"2023","journal-title":"ACM Trans. Inf. Syst."},{"key":"10.1016\/j.ins.2026.123525_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.dss.2023.114062","article-title":"Cognitive process-driven model design: a deep learning recommendation model with textual review and context","volume":"176","author":"Wang","year":"2024","journal-title":"Decis. Support Syst."},{"key":"10.1016\/j.ins.2026.123525_bib0070","series-title":"Proceedings of the 7th ACM Conference on Recommender Systems, RecSys \u201913","first-page":"165","article-title":"Hidden factors and hidden topics: understanding rating dimensions with review text","author":"McAuley","year":"2013"},{"key":"10.1016\/j.ins.2026.123525_bib0075","series-title":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","first-page":"425","article-title":"Joint deep modeling of users and items using reviews for recommendation","author":"Zheng","year":"2017"},{"key":"10.1016\/j.ins.2026.123525_bib0080","series-title":"Proceedings of the 2018 World Wide Web Conference","first-page":"1583","article-title":"Neural attentional rating regression with review-level explanations","author":"Chen","year":"2018"},{"key":"10.1016\/j.ins.2026.123525_bib0085","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"2309","article-title":"Multi-pointer co-attention networks for recommendation","author":"Tay","year":"2018"},{"key":"10.1016\/j.ins.2026.123525_bib0090","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1016\/j.ins.2019.10.038","article-title":"Recommendation system exploiting aspect-based opinion mining with deep learning method","volume":"512","author":"Da\u2019u","year":"2020","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123525_bib0095","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.ins.2018.04.009","article-title":"Leveraging sentiment analysis at the aspects level to predict ratings of reviews","volume":"451","author":"Qiu","year":"2018","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123525_bib0100","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1007\/s11280-024-01291-2","article-title":"A survey on large language models for recommendation","volume":"27","author":"Wu","year":"2024","journal-title":"World Wide Web"},{"key":"10.1016\/j.ins.2026.123525_bib0105","series-title":"Advances in Information Retrieval","first-page":"494","article-title":"Genrec: large language model for generative recommendation","author":"Ji","year":"2024"},{"key":"10.1016\/j.ins.2026.123525_bib0110","series-title":"Proceedings of the 17th ACM International Conference on Web Search and Data Mining","first-page":"452","article-title":"Once: boosting content-based recommendation with both open- and closed-source large language models","author":"Liu","year":"2024"},{"key":"10.1016\/j.ins.2026.123525_bib0115","doi-asserted-by":"crossref","first-page":"44:1","DOI":"10.1145\/3704999","article-title":"Understanding before recommendation: semantic aspect-aware review exploitation via large language models","volume":"43","author":"Liu","year":"2025","journal-title":"ACM Trans. Inf. Syst."},{"key":"10.1016\/j.ins.2026.123525_bib0120","series-title":"Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","first-page":"1480","article-title":"Hierarchical attention networks for document classification","author":"Yang","year":"2016"},{"key":"10.1016\/j.ins.2026.123525_bib0125","series-title":"Proceedings of the 28th ACM International Conference on Information and Knowledge Management","first-page":"2615","article-title":"Mind: a multi-interest network with dynamic routing for recommendation at Tmall","author":"Li","year":"2019"},{"key":"10.1016\/j.ins.2026.123525_bib0130","series-title":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"726","article-title":"Self-supervised graph learning for recommendation","author":"Wu","year":"2021"},{"key":"10.1016\/j.ins.2026.123525_bib0135","series-title":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","first-page":"1294","article-title":"Are graph augmentations necessary?: simple graph contrastive learning for recommendation","author":"Yu","year":"2022"},{"key":"10.1016\/j.ins.2026.123525_bib0140","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Lightgcl: simple yet effective graph contrastive learning for recommendation","author":"Cai","year":"2023"},{"key":"10.1016\/j.ins.2026.123525_bib0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.121732","article-title":"Nfgcl: a negative-sampling-free graph contrastive learning framework for recommendation","volume":"695","author":"Xiao","year":"2025","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123525_bib0150","series-title":"Proceedings of the 41st International Conference on Machine Learning, Volume 235 of Proceedings of Machine Learning Research","first-page":"47579","article-title":"Community-invariant graph contrastive learning","author":"Tan","year":"2024"},{"key":"10.1016\/j.ins.2026.123525_bib0155","first-page":"25:1","article-title":"Review-based recommender systems: a survey of approaches, challenges and future perspectives","volume":"58","author":"Hasan","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.ins.2026.123525_bib0160","author":"Liu"},{"key":"10.1016\/j.ins.2026.123525_bib0165","first-page":"996","article-title":"Understanding and predicting users\u2019 rating behavior: a cognitive perspective","volume":"32","author":"Li","year":"2020","journal-title":"INFORMS J. Comput."},{"key":"10.1016\/j.ins.2026.123525_bib0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.tourman.2025.105294","article-title":"Calculating tourist sentiment ambivalence through aspect-level sentiment analysis: infusing tourism domain knowledge into a pre-trained language model","volume":"113","author":"Yang","year":"2026","journal-title":"Tour. Manag."},{"key":"10.1016\/j.ins.2026.123525_bib0175","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1177\/0539018405058216","article-title":"What are emotions? And how can they be measured?","volume":"44","author":"Scherer","year":"2005","journal-title":"Soc. Sci. Inf."},{"key":"10.1016\/j.ins.2026.123525_bib0180","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1162\/coli_a_00364","article-title":"Argument mining: a survey","volume":"45","author":"Lawrence","year":"2019","journal-title":"Comput. Linguist."},{"key":"10.1016\/j.ins.2026.123525_bib0185","series-title":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing","first-page":"107","article-title":"Rationalizing neural predictions","author":"Lei","year":"2016"},{"key":"10.1016\/j.ins.2026.123525_bib0190","series-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","first-page":"4171","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"key":"10.1016\/j.ins.2026.123525_bib0195","series-title":"Proceedings of the Eleventh ACM Conference on Recommender Systems","first-page":"297","article-title":"Interpretable convolutional neural networks with dual local and global attention for review rating prediction","author":"Seo","year":"2017"},{"key":"10.1016\/j.ins.2026.123525_bib0200","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1287\/ijoc.2023.0131","article-title":"From interaction to prediction: a multi-interactive attention-based approach to product rating prediction","volume":"38","author":"Yu","year":"2025","journal-title":"INFORMS J. Comput."},{"key":"10.1016\/j.ins.2026.123525_bib0205","series-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","first-page":"4443","article-title":"ERASER: a benchmark to evaluate rationalized NLP models","author":"DeYoung","year":"2020"},{"key":"10.1016\/j.ins.2026.123525_bib0210","series-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","first-page":"4198","article-title":"Towards faithfully interpretable NLP systems: how should we define and evaluate faithfulness?","author":"Jacovi","year":"2020"},{"key":"10.1016\/j.ins.2026.123525_bib0215","series-title":"Proceedings of the 14th ACM Conference on Recommender Systems","first-page":"770","article-title":"Efficiency-effectiveness trade-offs in recommendation systems","author":"Paun","year":"2020"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526004561?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526004561?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T20:33:51Z","timestamp":1777408431000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526004561"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":43,"alternative-id":["S0020025526004561"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123525","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Decomposing preferences with a large language model for accurate and interpretable rating prediction","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123525","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123525"}}