{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:44:10Z","timestamp":1780609450109,"version":"3.54.1"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"34","license":[{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:00:00Z","timestamp":1726272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s00521-024-10364-2","type":"journal-article","created":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T08:02:47Z","timestamp":1726300967000},"page":"21723-21742","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Chain-of-thought prompting empowered generative user modeling for personalized recommendation"],"prefix":"10.1007","volume":"36","author":[{"given":"Fan","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7695-4538","authenticated-orcid":false,"given":"Yong","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gangmin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Terry R.","family":"Payne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ka Lok","family":"Man","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,14]]},"reference":[{"key":"10364_CR1","doi-asserted-by":"crossref","unstructured":"Davidson J, Liebald B, Liu J, et al. (2010) The YouTube video recommendation system. In Proceedings of the fourth ACM conference on recommender systems. pp 293\u2013296","DOI":"10.1145\/1864708.1864770"},{"key":"10364_CR2","doi-asserted-by":"crossref","unstructured":"Jin X, Zhou Y, Mobasher B. (2005) Task-oriented web user modeling for recommendation. In International conference on user modeling. Berlin, Heidelberg: Springer Berlin Heidelberg, pp 109\u2013118.","DOI":"10.1007\/11527886_15"},{"key":"10364_CR3","doi-asserted-by":"crossref","unstructured":"Zhang Y, Koren J. (2007) Efficient bayesian hierarchical user modeling for recommendation system. In Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval. pp 47\u201354.","DOI":"10.1145\/1277741.1277752"},{"key":"10364_CR4","doi-asserted-by":"publisher","first-page":"5683","DOI":"10.1007\/s00521-021-06706-z","volume":"34","author":"X Zhao","year":"2022","unstructured":"Zhao X, Jin Z, Liu Y et al (2022) Heterogeneous information network embedding for user behavior analysis on social media. Neural Comput Appl 34:5683\u20135699. https:\/\/doi.org\/10.1007\/s00521-021-06706-z","journal-title":"Neural Comput Appl"},{"issue":"4","key":"10364_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3352592","volume":"37","author":"X Lin","year":"2019","unstructured":"Lin X, Zhang M, Liu Y et al (2019) Enhancing personalized recommendation by implicit preference communities modeling. ACM Trans Inf Syst (TOIS) 37(4):1\u201332","journal-title":"ACM Trans Inf Syst (TOIS)"},{"key":"10364_CR6","doi-asserted-by":"crossref","unstructured":"Yuan F, He X, Karatzoglou A, et al. (2020) Parameter-efficient transfer from sequential behaviors for user modeling and recommendation. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. pp 1469\u20131478","DOI":"10.1145\/3397271.3401156"},{"key":"10364_CR7","doi-asserted-by":"crossref","unstructured":"Zhu N, Cao J, Liu Y, et al. (2020) Sequential modeling of hierarchical user intention and preference for next-item recommendation. In Proceedings of the 13th international conference on web search and data mining. 807\u2013815","DOI":"10.1145\/3336191.3371840"},{"key":"10364_CR8","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.neucom.2019.12.089","volume":"385","author":"X Zhou","year":"2020","unstructured":"Zhou X, Guo G, Sun Z et al (2020) Multi-facet user preference learning for fine-grained item recommendation. Neurocomputing 385:258\u2013268","journal-title":"Neurocomputing"},{"key":"10364_CR9","doi-asserted-by":"crossref","unstructured":"Guo X, Shi C, Liu C. (2020) Intention modeling from ordered and unordered facets for sequential recommendation. In Proceedings of the web conference 2020. pp 1127\u20131137","DOI":"10.1145\/3366423.3380190"},{"issue":"11","key":"10364_CR10","doi-asserted-by":"publisher","first-page":"5403","DOI":"10.1109\/TKDE.2021.3050571","volume":"34","author":"H Li","year":"2021","unstructured":"Li H, Wang X, Zhang Z et al (2021) Intention-aware sequential recommendation with structured intent transition. IEEE Trans Knowl Data Eng 34(11):5403\u20135414","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"10364_CR11","first-page":"1","volume":"39","author":"C Wang","year":"2020","unstructured":"Wang C, Ma W, Zhang M et al (2020) Toward dynamic user intention: Temporal evolutionary effects of item relations in sequential recommendation. ACM Trans Inf Syst (TOIS) 39(2):1\u201333","journal-title":"ACM Trans Inf Syst (TOIS)"},{"key":"10364_CR12","unstructured":"Touvron H, Lavril T, Izacard G, et al. (2023) LLaMA: open and efficient foundation language models. https:\/\/arxiv.org\/abs\/2302.13971"},{"key":"10364_CR13","unstructured":"Zhang S, Roller S, Goyal N, et al. (2022) Opt: open pre-trained transformer language models. arXiv preprint arXiv:2205.01068"},{"key":"10364_CR14","unstructured":"Zhao WX, Zhou K, Li J, et al. (2023) A survey of large language models. arXiv preprint arXiv:2303.18223"},{"key":"10364_CR15","unstructured":"Cui Z, Ma J, Zhou C, et al. (2022) M6-rec: Generative pretrained language models are open-ended recommender systems. arXiv preprint arXiv:2205.08084"},{"key":"10364_CR16","doi-asserted-by":"crossref","unstructured":"Geng S, Liu S, Fu Z, et al. (2022) Recommendation as language processing (rlp): a unified pretrain, personalized prompt & predict paradigm (p5). In Proceedings of the 16th ACM conference on recommender systems. pp 299\u2013315","DOI":"10.1145\/3523227.3546767"},{"key":"10364_CR17","doi-asserted-by":"crossref","unstructured":"Wang X, Zhou K, Wen J R, et al. (2022) Towards unified conversational recommender systems via knowledge-enhanced prompt learning. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. pp 1929\u20131937","DOI":"10.1145\/3534678.3539382"},{"key":"10364_CR18","doi-asserted-by":"crossref","unstructured":"Hou Y, Zhang J, Lin Z, et al. (2023) Large language models are zero-shot rankers for recommender systems. arXiv preprint arXiv:2305.08845","DOI":"10.1007\/978-3-031-56060-6_24"},{"key":"10364_CR19","unstructured":"Gao Y, Sheng T, Xiang Y, et al. (2023) Chat-rec: towards interactive and explainable llms-augmented recommender system. arXiv preprint arXiv:2303.14524"},{"key":"10364_CR20","unstructured":"Wang L, Lim E P. (2023) Zero-Shot Next-Item Recommendation using Large Pretrained Language Models. arXiv preprint arXiv:2304.03153"},{"key":"10364_CR21","unstructured":"Kang W C, Ni J, Mehta N, et al. (2023) Do LLMs understand user preferences? evaluating LLMs On user rating prediction. arXiv preprint arXiv:2305.06474"},{"key":"10364_CR22","unstructured":"Wang W, Lin X, Feng F, et al. (2023) Generative recommendation: towards next-generation recommender paradigm. arXiv preprint arXiv:2304.03516"},{"key":"10364_CR23","unstructured":"Li J, Zhang W, Wang T, et al. (2023) GPT4Rec: a generative framework for personalized recommendation and user interests interpretation. arXiv preprint arXiv:2304.03879"},{"key":"10364_CR24","unstructured":"Liu Q, Chen N, Sakai T, et al. (2023) A first look at LLM-powered generative news recommendation. arXiv preprint arXiv:2305.06566"},{"key":"10364_CR25","unstructured":"Zhang J, Xie R, Hou Y, et al. (2023) Recommendation as instruction following: a large language model empowered recommendation approach. arXiv preprint arXiv:2305.07001"},{"key":"10364_CR26","doi-asserted-by":"crossref","unstructured":"Bao K, Zhang J, Zhang Y, et al. (2023) Tallrec: an effective and efficient tuning framework to align large language model with recommendation. arXiv preprint arXiv:2305.00447","DOI":"10.1145\/3604915.3608857"},{"key":"10364_CR27","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877\u20131901","journal-title":"Adv Neural Inf Process Syst"},{"key":"10364_CR28","doi-asserted-by":"crossref","unstructured":"Deldjoo, Yashar. (2024) Understanding biases in ChatGPT-based recommender systems: provider fairness, temporal stability, and recency. arXiv preprint arXiv:2401.10545","DOI":"10.1145\/3690655"},{"key":"10364_CR29","doi-asserted-by":"crossref","unstructured":"Gao T, Fisch A, Chen D. (2020) Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723","DOI":"10.18653\/v1\/2021.acl-long.295"},{"key":"10364_CR30","doi-asserted-by":"crossref","unstructured":"Schick T, Sch\u00fctze H. (2020) Exploiting cloze questions for few shot text classification and natural language inference. arXiv preprint arXiv:2001.07676","DOI":"10.18653\/v1\/2021.eacl-main.20"},{"key":"10364_CR31","unstructured":"Zhao Z, Wallace E, Feng S, et al. (2021) Calibrate before use: Improving few-shot performance of language models. In International conference on machine learning. PMLR, pp 12697\u201312706"},{"key":"10364_CR32","doi-asserted-by":"crossref","unstructured":"Bianchi F, Terragni S, Hovy D. (2020) Pre-training is a hot topic: contextualized document embeddings improve topic coherence. arXiv preprint arXiv:2004.03974","DOI":"10.18653\/v1\/2021.acl-short.96"},{"key":"10364_CR33","first-page":"24824","volume":"35","author":"J Wei","year":"2022","unstructured":"Wei J, Wang X, Schuurmans D et al (2022) Chain-of-thought prompting elicits reasoning in large language models. Adv Neural Inf Process Syst 35:24824\u201324837","journal-title":"Adv Neural Inf Process Syst"},{"key":"10364_CR34","unstructured":"Wang X, Wei J, Schuurmans D, et al. (2022) Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171"},{"key":"10364_CR35","doi-asserted-by":"crossref","unstructured":"Dai S, Shao N, Zhao H, et al. (2023) Uncovering ChatGPT's capabilities in recommender systems. arXiv preprint arXiv:2305.02182","DOI":"10.1145\/3604915.3610646"},{"key":"10364_CR36","doi-asserted-by":"crossref","unstructured":"Chen C, Zhang M, Liu Y, et al. (2019) Social attentional memory network: Modeling aspect-and friend-level differences in recommendation. In Proceedings of the twelfth ACM international conference on web search and data mining. pp 177\u2013185","DOI":"10.1145\/3289600.3290982"},{"key":"10364_CR37","doi-asserted-by":"crossref","unstructured":"Huang X, Fang Q, Qian S, et al. (2019) Explainable interaction-driven user modeling over knowledge graph for sequential recommendation. In Proceedings of the 27th ACM international conference on multimedia. pp 548\u2013556","DOI":"10.1145\/3343031.3350893"},{"key":"10364_CR38","doi-asserted-by":"crossref","unstructured":"Wang C, Zhu Y, Liu H, et al. (2021) Enhancing user interest modeling with knowledge-enriched itemsets for sequential recommendation. In Proceedings of the 30th ACM international conference on information & knowledge management. pp 1889\u20131898","DOI":"10.1145\/3459637.3482256"},{"key":"10364_CR39","doi-asserted-by":"crossref","unstructured":"Qi T, Wu F, Wu C, et al. (2021) HieRec: hierarchical user interest modeling for personalized news recommendation. arXiv preprint arXiv:2106.04408","DOI":"10.18653\/v1\/2021.acl-long.423"},{"key":"10364_CR40","doi-asserted-by":"crossref","unstructured":"Wu C, Wu F, Huang Y, et al. (2021) User-as-graph: user modeling with heterogeneous graph pooling for news recommendation. In IJCAI. pp 1624\u20131630","DOI":"10.24963\/ijcai.2021\/224"},{"issue":"5","key":"10364_CR41","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1089\/big.2021.0395","volume":"10","author":"F Yang","year":"2022","unstructured":"Yang F, Li G, Yue Y (2022) Hierarchical user intention-preference for sequential recommendation with relation-aware heterogeneous information network embedding. Big Data 10(5):466\u2013478","journal-title":"Big Data"},{"key":"10364_CR42","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-023-08694-8","author":"G Suganeshwari","year":"2023","unstructured":"Suganeshwari G, Peer Mohamed SI, Sugumaran V (2023) A graph-based collaborative filtering algorithm combining implicit user preference and explicit time-related feedback. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-023-08694-8","journal-title":"Neural Comput Appl"},{"key":"10364_CR43","unstructured":"Wang R, Lu W. (2022) Modeling multi-interest news sequence for news recommendation. arXiv preprint arXiv:2207.07331"},{"key":"10364_CR44","unstructured":"Portman F, Ragain S, El-Kishky A. (2022) MiCRO: multi-interest candidate retrieval online. arXiv preprint arXiv:2210.16271"},{"key":"10364_CR45","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3264550","author":"F Yang","year":"2023","unstructured":"Yang F, Yue Y, Li G et al (2023) KEMIM: knowledge-enhanced user multi-interest modeling for recommender systems. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2023.3264550","journal-title":"IEEE Access"},{"key":"10364_CR46","doi-asserted-by":"crossref","unstructured":"Radlinski F, Balog K, Diaz F, et al. (2022) On Natural Language User Profiles for Transparent and Scrutable Recommendation. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval. pp 2863\u20132874.","DOI":"10.1145\/3477495.3531873"},{"key":"10364_CR47","doi-asserted-by":"crossref","unstructured":"Balog K, Radlinski F, Arakelyan S. (2019) Transparent, scrutable and explainable user models for personalized recommendation. In Proceedings of the 42nd international acm sigir conference on research and development in information retrieval. pp 265\u2013274","DOI":"10.1145\/3331184.3331211"},{"key":"10364_CR48","doi-asserted-by":"crossref","unstructured":"Bogers T, Koolen M. (2017) Defining and supporting narrative-driven recommendation. In Proceedings of the eleventh ACM conference on recommender systems. pp 238\u2013242","DOI":"10.1145\/3109859.3109893"},{"key":"10364_CR49","doi-asserted-by":"crossref","unstructured":"Steck H. (2013) Evaluation of recommendations: rating-prediction and ranking. In Proceedings of the 7th ACM conference on Recommender systems. pp 213\u2013220","DOI":"10.1145\/2507157.2507160"},{"key":"10364_CR50","doi-asserted-by":"crossref","unstructured":"Liu V, Chilton L B. (2022) Design guidelines for prompt engineering text-to-image generative models. In Proceedings of the 2022 CHI conference on human factors in computing systems. pp 1-23","DOI":"10.1145\/3491102.3501825"},{"key":"10364_CR51","unstructured":"Liu J, Liu C, Lv R, et al. (2023) Is chatgpt a good recommender? a preliminary study. arXiv preprint arXiv:2304.10149"},{"key":"10364_CR52","doi-asserted-by":"crossref","unstructured":"Zhou K, Zhou Y, Zhao W X, et al. (2020) Towards topic-guided conversational recommender system. arXiv preprint arXiv:2010.04125","DOI":"10.18653\/v1\/2020.coling-main.365"},{"issue":"8","key":"10364_CR53","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30\u201337","journal-title":"Computer"},{"key":"10364_CR54","doi-asserted-by":"crossref","unstructured":"Cheng H T, Koc L, Harmsen J, et al. (2016) Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. pp 7\u201310","DOI":"10.1145\/2988450.2988454"},{"key":"10364_CR55","unstructured":"Rendle S, Freudenthaler C, Gantner Z, et al. (2012) BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618"},{"key":"10364_CR56","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, et al. (2015) Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939"},{"key":"10364_CR57","doi-asserted-by":"crossref","unstructured":"Sun F, Liu J, Wu J, et al. (2019) BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management. pp 1441\u20131450","DOI":"10.1145\/3357384.3357895"},{"key":"10364_CR58","doi-asserted-by":"crossref","unstructured":"Kang W C, McAuley J. (2018) Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE pp: 197\u2013206","DOI":"10.1109\/ICDM.2018.00035"},{"key":"10364_CR59","doi-asserted-by":"crossref","unstructured":"Dong L, Huang S, Wei F, et al. (2017) Learning to generate product reviews from attributes. In Proceedings of the 15th conference of the European chapter of the association for computational linguistics: Vol 1, Long Papers. pp 623\u2013632","DOI":"10.18653\/v1\/E17-1059"},{"key":"10364_CR60","doi-asserted-by":"crossref","unstructured":"Li P, Wang Z, Ren Z, et al. (2017) Neural rating regression with abstractive tips generation for recommendation. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. pp 345\u2013354","DOI":"10.1145\/3077136.3080822"},{"key":"10364_CR61","doi-asserted-by":"crossref","unstructured":"Li L, Zhang Y, Chen L. (2021) Personalized transformer for explainable recommendation. arXiv preprint arXiv:2105.11601","DOI":"10.18653\/v1\/2021.acl-long.383"},{"key":"10364_CR62","doi-asserted-by":"crossref","unstructured":"Sanner S, Krisztian B, Filip R, et al. (2023) Large language models are competitive near cold-start recommenders for language-and item-based preferences. In Proceedings of the 17th ACM conference on recommender systems. pp 890\u2013896","DOI":"10.1145\/3604915.3608845"},{"key":"10364_CR63","doi-asserted-by":"crossref","unstructured":"Zhou K, Hui W, Wayne X Z, et al. (2020) S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proceedings of the 29th ACM international conference on information & knowledge management. pp 1893\u20131902","DOI":"10.1145\/3340531.3411954"},{"key":"10364_CR64","unstructured":"Zhou, Denny, Nathanael Sch\u00e4rli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans et al. (2022) Least-to-most prompting enables complex reasoning in large language models. arXiv preprint arXiv:2205.10625"},{"key":"10364_CR65","doi-asserted-by":"crossref","unstructured":"Papineni K, Salim R, Todd W, et al. (2002) Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting of the association for computational linguistics. pp 311\u2013318","DOI":"10.3115\/1073083.1073135"},{"key":"10364_CR66","unstructured":"Lin, Chin-Yew. (2004) Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. pp 74\u201381"},{"key":"10364_CR67","doi-asserted-by":"crossref","unstructured":"Acharya A, Brijraj S, and Naoyuki O. (2023) LLM based generation of item-description for recommendation system. In Proceedings of the 17th ACM conference on recommender systems. pp 1204\u20131207","DOI":"10.1145\/3604915.3610647"},{"key":"10364_CR68","doi-asserted-by":"publisher","first-page":"126734","DOI":"10.1016\/j.neucom.2023.126734","volume":"557","author":"X Wang","year":"2023","unstructured":"Wang X, Wang D, Yu D et al (2023) Intent-aware graph neural network for point-of-interest embedding and recommendation. Neurocomputing 557:126734","journal-title":"Neurocomputing"},{"key":"10364_CR69","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neucom.2021.02.015","volume":"441","author":"J Zhang","year":"2021","unstructured":"Zhang J, Wang D, Dongjin Yu (2021) TLSAN: time-aware long-and short-term attention network for next-item recommendation. Neurocomputing 441:179\u2013191","journal-title":"Neurocomputing"},{"key":"10364_CR70","doi-asserted-by":"crossref","unstructured":"Li, Lei, Yongfeng Zhang, and Li Chen. (2023) Prompt distillation for efficient LLM-based recommendation. In Proceedings of the 32nd ACM international conference on information and knowledge management. pp 1348\u20131357","DOI":"10.1145\/3583780.3615017"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10364-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10364-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10364-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T09:09:51Z","timestamp":1732352991000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10364-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,14]]},"references-count":70,"journal-issue":{"issue":"34","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["10364"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10364-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,14]]},"assertion":[{"value":"3 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The datasets used and analyzed during the current study are available from the authors upon reasonable request.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}