{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:08:05Z","timestamp":1779174485839,"version":"3.51.4"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T00:00:00Z","timestamp":1725148800000},"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":["J. Comput. Sci. Technol."],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s11390-024-3522-9","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T22:33:14Z","timestamp":1733437994000},"page":"1138-1152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Intent-Aware Graph-Level Embedding Learning Based Recommendation"],"prefix":"10.1007","volume":"39","author":[{"given":"Peng-Yi","family":"Hao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Si-Hao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cong","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"3522_CR1","doi-asserted-by":"publisher","first-page":"2212","DOI":"10.1145\/3534678.3539475","volume-title":"Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"M Yang","year":"2022","unstructured":"Yang M, Li Z, Zhou M, Liu J, Irwin K. HICf: Hyperbolic informative collaborative filtering. In Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2022, pp.2212\u20132221. DOI: https:\/\/doi.org\/10.1145\/3534678.3539475."},{"key":"3522_CR2","doi-asserted-by":"publisher","first-page":"2100","DOI":"10.1145\/3534678.3539473","volume-title":"Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"L Xia","year":"2022","unstructured":"Xia L, Huang C, Zhang C. Self-supervised hypergraph transformer for recommender systems. In Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2022, pp.2100\u20132109. DOI: https:\/\/doi.org\/10.1145\/3534678.3539473."},{"key":"3522_CR3","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1145\/3097983.3098036","volume-title":"Proc. the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Y Dong","year":"2017","unstructured":"Dong Y, Chawla N V, Swami A. Metapath2vec: Scalable representation learning for heterogeneous networks. In Proc. the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2017, pp.135\u2013144. DOI: https:\/\/doi.org\/10.1145\/3097983.3098036."},{"key":"3522_CR4","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1145\/3132847.3132953","volume-title":"Proc. the 2017 ACM Conference on Information and Knowledge Management","author":"T Y Fu","year":"2017","unstructured":"Fu T Y, Lee W C, Lei Z. HIN2Vec: Explore meta-paths in heterogeneous information networks for representation learning. In Proc. the 2017 ACM Conference on Information and Knowledge Management, Nov. 2017, pp.1797\u20131806. DOI: https:\/\/doi.org\/10.1145\/3132847.3132953."},{"key":"3522_CR5","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.1145\/3340531.3412729","volume-title":"Proc. the 29th ACM International Conference on Information & Knowledge Management","author":"Y Feng","year":"2020","unstructured":"Feng Y, Lv F, Hu B, Sun F, Kuang K, Liu Y, Liu Q, Ou W. MTBRN: Multiplex target-behavior relation enhanced network for click-through rate prediction. In Proc. the 29th ACM International Conference on Information & Knowledge Management, Oct. 2020, pp.2421\u20132428. DOI: https:\/\/doi.org\/10.1145\/3340531.3412729."},{"key":"3522_CR6","doi-asserted-by":"publisher","first-page":"2050","DOI":"10.1145\/3534678.3539461","volume-title":"Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"K Wu","year":"2022","unstructured":"Wu K, Bian W, Chan Z, Ren L, Xiang S, Han S G, Deng H, Zheng B. Adversarial gradient driven exploration for deep click-through rate prediction. In Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2022, pp.2050\u20132058. DOI:https:\/\/doi.org\/10.1145\/3534678.3539461."},{"key":"3522_CR7","doi-asserted-by":"publisher","first-page":"3458","DOI":"10.1145\/3459637.3482216","volume-title":"Proc. the 30th ACM International Conference on Information & Knowledge Management","author":"G Wang","year":"2021","unstructured":"Wang G, Zhong T, Xu X, Zhang K, Zhou F, Wang Y. Vector-quantized autoencoder with copula for collaborative filtering. In Proc. the 30th ACM International Conference on Information & Knowledge Management, Nov. 2021, pp.3458\u20133462. DOI: https:\/\/doi.org\/10.1145\/3459637.3482216."},{"key":"3522_CR8","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1145\/3038912.3052569","volume-title":"Proc. the 26th International Conference on World Wide Web","author":"X He","year":"2017","unstructured":"He X, Liao L, Zhang H, Nie L, Hu X, Chua T S. Neural collaborative filtering. In Proc. the 26th International Conference on World Wide Web, Apr. 2017, pp.173\u2013182. DOI: https:\/\/doi.org\/10.1145\/3038912.3052569."},{"key":"3522_CR9","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1145\/3488560.3498520","volume-title":"Proc. the 15th ACM International Conference on Web Search and Data Mining","author":"Z Wang","year":"2022","unstructured":"Wang Z, Zhao H, Shi C. Profiling the design space for graph neural networks based collaborative filtering. In Proc. the 15th ACM International Conference on Web Search and Data Mining, Feb. 2022, pp.1109\u20131119. DOI: https:\/\/doi.org\/10.1145\/3488560.3498520."},{"key":"3522_CR10","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1145\/3397271.3401057","volume-title":"Proc. the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"J Gong","year":"2020","unstructured":"Gong J, Wang S, Wang J, Feng W, Hao P, Tang J, Yu P S. Attentional graph convolutional networks for knowledge concept recommendation in MOOCs in a heterogeneous view. In Proc. the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2020, pp.79\u201388. DOI: https:\/\/doi.org\/10.1145\/3397271.3401057."},{"issue":"2","key":"3522_CR11","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1109\/TKDE.2021.3101356","volume":"35","author":"Y Yang","year":"2023","unstructured":"Yang Y, Guan Z, Li J, Zhao W, Cui J, Wang Q. Interpretable and efficient heterogeneous graph convolutional network. IEEE Trans. Knowledge and Data Engineering, 2023, 35(2): 1637\u20131650. DOI: https:\/\/doi.org\/10.1109\/TKDE.2021.3101356.","journal-title":"IEEE Trans. Knowledge and Data Engineering"},{"key":"3522_CR12","doi-asserted-by":"publisher","first-page":"2331","DOI":"10.1145\/3366423.3380297","volume-title":"Proc. the 29th International Conference on World Wide Web","author":"X Fu","year":"2020","unstructured":"Fu X, Zhang J, Meng Z, King I. MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proc. the 29th International Conference on World Wide Web, Apr. 2020, pp.2331\u20132341. DOI: https:\/\/doi.org\/10.1145\/3366423.3380297."},{"issue":"6","key":"3522_CR13","doi-asserted-by":"publisher","first-page":"4093","DOI":"10.1007\/s11280-023-01224-5","volume":"26","author":"P Hao","year":"2023","unstructured":"Hao P, Qian Z, Wang S, Bai C. Community aware graph embedding learning for item recommendation. World Wide Web, 2023, 26(6): 4093\u20134108. DOI: https:\/\/doi.org\/10.1007\/s11280-023-01224-5.","journal-title":"World Wide Web"},{"key":"3522_CR14","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1145\/3331184.3331283","volume-title":"Proc. the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"F Li","year":"2019","unstructured":"Li F, Chen Z, Wang P, Ren Y, Zhang D, Zhu X. Graph intention network for click-through rate prediction in sponsored search. In Proc. the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2019, pp.961\u2013964. DOI: https:\/\/doi.org\/10.1145\/3331184.3331283."},{"key":"3522_CR15","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1145\/3488560.3498458","volume-title":"Proc. the 15th ACM International Conference on Web Search and Data Mining","author":"W Jiang","year":"2022","unstructured":"Jiang W, Jiao Y, Wang Q, Liang C, Guo L, Zhang Y, Sun Z, Xiong Y, Zhu Y. Triangle graph interest network for click-through rate prediction. In Proc. the 15th ACM International Conference on Web Search and Data Mining, Feb. 2022, pp.401\u2013409. DOI: https:\/\/doi.org\/10.1145\/3488560.3498458."},{"key":"3522_CR16","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1109\/ICDE.2019.00035","volume-title":"Proc. the 35th IEEE International Conference on Data Engineering","author":"T Chen","year":"2019","unstructured":"Chen T, Yin H, Chen H, Yan R, Nguyen Q V H, Li X. AIR: Attentional intention-aware recommender systems. In Proc. the 35th IEEE International Conference on Data Engineering, Apr. 2019, pp.304\u2013315. DOI: https:\/\/doi.org\/10.1109\/ICDE.2019.00035."},{"key":"3522_CR17","doi-asserted-by":"publisher","first-page":"2263","DOI":"10.1145\/3534678.3539342","volume-title":"Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Y Yang","year":"2022","unstructured":"Yang Y, Huang C, Xia L, Liang Y, Yu Y, Li C. Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2022, pp.2263\u20132274. DOI: https:\/\/doi.org\/10.1145\/3534678.3539342."},{"key":"3522_CR18","doi-asserted-by":"publisher","first-page":"109936","DOI":"10.1016\/j.knosys.2022.109936","volume":"258","author":"R Wang","year":"2022","unstructured":"Wang R, Yang N, Yu P S. Learning aspect-level complementarity for intent-aware complementary recommendation. Knowledge-Based Systems, 2022, 258:109936. DOI: https:\/\/doi.org\/10.1016\/j.knosys.2022.109936.","journal-title":"Knowledge-Based Systems"},{"key":"3522_CR19","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1145\/3488560.3498524","volume-title":"Proc. the 15th ACM International Conference on Web Search and Data Mining","author":"J Guo","year":"2022","unstructured":"Guo J, Yang Y, Song X, Zhang Y, Wang Y, Bai J, Zhang Y. Learning multi-granularity consecutive user intent unit for session-based recommendation. In Proc. the 15th ACM International Conference on Web Search and Data Mining, Feb. 2022, pp.343\u2013352. DOI: https:\/\/doi.org\/10.1145\/3488560.3498524."},{"key":"3522_CR20","doi-asserted-by":"publisher","first-page":"2478","DOI":"10.1145\/3292500.3330673","volume-title":"Proc. the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"S Fan","year":"2019","unstructured":"Fan S, Zhu J, Han X, Shi C, Hu L, Ma B, Li Y. Metapath-guided heterogeneous graph neural network for intent recommendation. In Proc. the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2019, pp.2478\u20132486. DOI: https:\/\/doi.org\/10.1145\/3292500.3330673."},{"key":"3522_CR21","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1145\/3539618.3591702","volume-title":"Proc. the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"J Li","year":"2023","unstructured":"Li J, Sun P, Wang Z, Ma W, Li Y, Zhang M, Feng Z. Intent-aware ranking ensemble for personalized recommendation. In Proc. the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2023, pp.1004\u20131013. DOI: https:\/\/doi.org\/10.1145\/3539618.3591702."},{"key":"3522_CR22","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1145\/2623330.2623732","volume-title":"Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"B Perozzi","year":"2014","unstructured":"Perozzi B, Al-Rfou R, Skiena S. DeepWalk: Online learning of social representations. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2014, pp.701\u2013710. DOI: https:\/\/doi.org\/10.1145\/2623330.2623732."},{"key":"3522_CR23","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1145\/2939672.2939754","volume-title":"Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"A Grover","year":"2016","unstructured":"Grover A, Leskovec J. Node2vec: Scalable feature learning for networks. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2016, pp.855\u2013864. DOI: https:\/\/doi.org\/10.1145\/2939672.2939754."},{"key":"3522_CR24","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1145\/3219819.3219869","volume-title":"Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"J Wang","year":"2018","unstructured":"Wang J, Huang P, Zhao H, Zhang Z, Zhao B, Lee D L. Billion-scale commodity embedding for E-commerce recommendation in Alibaba. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Jul. 2018, pp.839\u2013848. DOI: https:\/\/doi.org\/10.1145\/3219819.3219869."},{"key":"3522_CR25","volume-title":"Variational graph auto-encoders","author":"T N Kipf","year":"2016","unstructured":"Kipf T N, Welling M. Variational graph auto-encoders. arXiv: 1611.07308, 2016. https:\/\/arxiv.org\/abs\/1611.07308, Sept. 2024."},{"key":"3522_CR26","volume-title":"Proc. the 7th International Conference on Learning Representations","author":"P Velickovic","year":"2019","unstructured":"Velickovic P, Fedus W, Hamilton W L, Li\u00f2 P, Bengio Y, Hjelm R D. Deep graph infomax. In Proc. the 7th International Conference on Learning Representations, May 2019."},{"key":"3522_CR27","volume-title":"Proc. the 5th International Conference on Learning Representations","author":"T N Kipf","year":"2017","unstructured":"Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In Proc. the 5th International Conference on Learning Representations, Apr. 2017."},{"key":"3522_CR28","volume-title":"Graph attention networks","author":"P Veli\u010dkovi\u0107","year":"2017","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y. Graph attention networks. arXiv: 1710.10903, 2017. https:\/\/arxiv.org\/abs\/1710.10903, Sept. 2024."},{"key":"3522_CR29","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869","volume-title":"Proc. the 31st International Conference on Neural Information Processing Systems","author":"W L Hamilton","year":"2017","unstructured":"Hamilton W L, Ying Z, Leskovec J. Inductive representation learning on large graphs. In Proc. the 31st International Conference on Neural Information Processing Systems, Dec. 2017. DOI: https:\/\/doi.org\/10.5555\/3294771.3294869."},{"key":"3522_CR30","volume-title":"Proc. the 7th International Conference on Learning Representations","author":"K Xu","year":"2019","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? In Proc. the 7th International Conference on Learning Representations, May 2019."},{"key":"3522_CR31","volume-title":"Proc. the 1st International Conference on Learning Representations","author":"T Mikolov","year":"2013","unstructured":"Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. In Proc. the 1st International Conference on Learning Representations, May 2013."},{"key":"3522_CR32","series-title":"Technical Report","volume-title":"The PageRank citation ranking: Bringing order to the web","author":"L Page","year":"1999","unstructured":"Page L, Brin S, Motwani R, Winograd T. The PageRank citation ranking: Bringing order to the web. Technical Report, Stanford InfoLab, 1999. http:\/\/ilpubs.stanford.edu:8090\/422\/, Sept. 2024."},{"key":"3522_CR33","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1145\/3292500.3330855","volume-title":"Proc. the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"N Park","year":"2019","unstructured":"Park N, Kan A, Dong X L, Zhao T, Faloutsos C. Estimating node importance in knowledge graphs using graph neural networks. In Proc. the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2019, pp.596\u2013606. DOI: https:\/\/doi.org\/10.1145\/3292500.3330855."},{"key":"3522_CR34","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.patrec.2020.08.015","volume":"138","author":"Y Liu","year":"2020","unstructured":"Liu Y, Wang Q, Wang X, Zhang F, Geng L, Wu J, Xiao Z. Community enhanced graph convolutional networks. Pattern Recognition Letters, 2020, 138:462\u2013468. DOI: https:\/\/doi.org\/10.1016\/j.patrec.2020.08.015.","journal-title":"Pattern Recognition Letters"},{"issue":"23","key":"3522_CR35","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1073\/pnas.0601602103","volume":"103","author":"M E J Newman","year":"2006","unstructured":"Newman M E J. Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(23): 8577\u20138582. DOI: https:\/\/doi.org\/10.1073\/pnas.0601602103.","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"3522_CR36","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1145\/3331184.3331267","volume-title":"Proc. the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"X Wang","year":"2019","unstructured":"Wang X, He X, Wang M, Feng F, Chua T S. Neural graph collaborative filtering. In Proc. the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2019, pp.165\u2013174. DOI: https:\/\/doi.org\/10.1145\/3331184.3331267."},{"key":"3522_CR37","doi-asserted-by":"publisher","first-page":"2037","DOI":"10.1145\/3539618.3591994","volume-title":"Proc. the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Z Fan","year":"2023","unstructured":"Fan Z, Xu K, Dong Z, Peng H, Zhang J, Yu P S. Graph collaborative signals denoising and augmentation for recommendation. In Proc. the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2023, pp.2037\u20132041. DOI: https:\/\/doi.org\/10.1145\/3539618.3591994."},{"key":"3522_CR38","doi-asserted-by":"publisher","first-page":"2098","DOI":"10.1145\/3511808.3557404","volume-title":"Proc. the 31st ACM International Conference on Information & Knowledge Management","author":"Z Wang","year":"2022","unstructured":"Wang Z, Liu H, Wei W, Hu Y, Mao X L, He S, Fang R, Chen D. Multi-level contrastive learning framework for sequential recommendation. In Proc. the 31st ACM International Conference on Information & Knowledge Management, Oct. 2022, pp.2098\u20132107. DOI: https:\/\/doi.org\/10.1145\/3511808.3557404."},{"key":"3522_CR39","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1109\/ICDM.2019.00165","volume-title":"Proc. the 2019 IEEE International Conference on Data Mining","author":"J Sun","year":"2019","unstructured":"Sun J, Zhang Y, Ma C, Coates M, Guo H, Tang R, He X. Multi-graph convolution collaborative filtering. In Proc. the 2019 IEEE International Conference on Data Mining, Nov. 2019, pp.1306\u20131311. DOI: https:\/\/doi.org\/10.1109\/ICDM.2019.00165."}],"container-title":["Journal of Computer Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-024-3522-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11390-024-3522-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-024-3522-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T23:02:18Z","timestamp":1733439738000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11390-024-3522-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["3522"],"URL":"https:\/\/doi.org\/10.1007\/s11390-024-3522-9","relation":{},"ISSN":["1000-9000","1860-4749"],"issn-type":[{"value":"1000-9000","type":"print"},{"value":"1860-4749","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9]]},"assertion":[{"value":"18 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"<b>Conflict of Interest<\/b> The authors declare that they have no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}}]}}