{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T04:30:40Z","timestamp":1758342640005,"version":"3.44.0"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"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":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s10489-025-06388-3","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:16:50Z","timestamp":1740460610000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A framework for solving bias in graph-based recommender systems with a causal perspective"],"prefix":"10.1007","volume":"55","author":[{"given":"Kewu","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guogang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linjia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianrong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"6388_CR1","doi-asserted-by":"crossref","unstructured":"Chen L, Wu L, Hong R, Zhang K, Wang M (2020) Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 27\u201334","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"6388_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11427-019-9817-6","volume":"63","author":"G Li","year":"2020","unstructured":"Li G, Liu H, Li G, Shen S, Tang H (2020) Lstm-based argument recommendation for non-api methods. Sci Chin Inf Sci 63:1\u201322","journal-title":"Sci Chin Inf Sci"},{"key":"6388_CR3","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1007\/s41019-020-00138-w","volume":"5","author":"J Chen","year":"2020","unstructured":"Chen J, Chen W, Huang J, Fang J, Li Z, Liu A, Zhao L (2020) Co-purchaser recommendation for online group buying. Data Sci Eng 5:280\u2013292","journal-title":"Data Sci Eng"},{"issue":"5","key":"6388_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3535101","volume":"55","author":"S Wu","year":"2022","unstructured":"Wu S, Sun F, Zhang W, Xie X, Cui B (2022) Graph neural networks in recommender systems: a survey. ACM Comput Surv 55(5):1\u201337","journal-title":"ACM Comput Surv"},{"issue":"1","key":"6388_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3158369","volume":"52","author":"S Zhang","year":"2019","unstructured":"Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"key":"6388_CR6","doi-asserted-by":"crossref","unstructured":"Xie X, Liu Z, Wu S, Sun F, Liu C, Chen J, Gao J, Cui B, Ding B (2021) Causcf: causal collaborative filtering for recommendation effect estimation. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 4253\u20134263","DOI":"10.1145\/3459637.3481901"},{"key":"6388_CR7","doi-asserted-by":"crossref","unstructured":"Pfadler A, Zhao H, Wang J, Wang L, Huang P, Lee DL (2020) Billion-scale recommendation with heterogeneous side information at taobao. In: 2020 IEEE 36th international conference on data engineering (ICDE). IEEE, pp 1667\u20131676","DOI":"10.1109\/ICDE48307.2020.00148"},{"key":"6388_CR8","doi-asserted-by":"crossref","unstructured":"Eksombatchai C, Jindal P, Liu JZ, Liu Y, Sharma R, Sugnet C, Ulrich M, Leskovec J (2018) Pixie: a system for recommending 3+ billion items to 200+ million users in real-time. In: Proceedings of the 2018 world wide web conference, pp 1775\u20131784","DOI":"10.1145\/3178876.3186183"},{"key":"6388_CR9","doi-asserted-by":"crossref","unstructured":"Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J (2018) Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 974\u2013983","DOI":"10.1145\/3219819.3219890"},{"issue":"7","key":"6388_CR10","doi-asserted-by":"publisher","first-page":"2966","DOI":"10.1287\/mnsc.2018.3093","volume":"65","author":"A Lambrecht","year":"2019","unstructured":"Lambrecht A, Tucker C (2019) Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads. Manag Sci 65(7):2966\u20132981","journal-title":"Manag Sci"},{"issue":"5","key":"6388_CR11","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1145\/2447976.2447990","volume":"56","author":"L Sweeney","year":"2013","unstructured":"Sweeney L (2013) Discrimination in online ad delivery. Commun ACM 56(5):44\u201354","journal-title":"Commun ACM"},{"key":"6388_CR12","doi-asserted-by":"crossref","unstructured":"Beutel A, Chen J, Doshi T, Qian H, Wei L, Wu Y, Heldt L, Zhao Z, Hong L, Chi EH, et al (2019) Fairness in recommendation ranking through pairwise comparisons. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2212\u20132220","DOI":"10.1145\/3292500.3330745"},{"key":"6388_CR13","doi-asserted-by":"crossref","unstructured":"Beigi G, Mosallanezhad A, Guo R, Alvari H, Nou A, Liu H (2020) Privacy-aware recommendation with private-attribute protection using adversarial learning. In: Proceedings of the 13th international conference on web search and data mining, pp 34\u201342","DOI":"10.1145\/3336191.3371832"},{"key":"6388_CR14","unstructured":"Burke R (2017) Multisided fairness for recommendation. arXiv:1707.00093"},{"key":"6388_CR15","doi-asserted-by":"crossref","unstructured":"Ge Y, Liu S, Gao R, Xian Y, Li Y, Zhao X, Pei C, Sun F, Ge J, Ou W, et al (2021) Towards long-term fairness in recommendation. In: Proceedings of the 14th ACM international conference on web search and data mining, pp 445\u2013453","DOI":"10.1145\/3437963.3441824"},{"key":"6388_CR16","unstructured":"Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. Adv Neural Inf Process Syst 29"},{"key":"6388_CR17","unstructured":"Yao S, Huang B (2017) Beyond parity: fairness objectives for collaborative filtering. Adv Neural Inf Process Syst 30"},{"key":"6388_CR18","doi-asserted-by":"crossref","unstructured":"Zhu Z, Hu X, Caverlee J (2018) Fairness-aware tensor-based recommendation. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 1153\u20131162","DOI":"10.1145\/3269206.3271795"},{"key":"6388_CR19","doi-asserted-by":"crossref","unstructured":"Khademi A, Lee S, Foley D, Honavar V (2019) Fairness in algorithmic decision making: An excursion through the lens of causality. In: The world wide web conference, pp 2907\u20132914","DOI":"10.1145\/3308558.3313559"},{"key":"6388_CR20","doi-asserted-by":"crossref","unstructured":"Zhang J, Bareinboim E (2018) Fairness in decision-making\u2014the causal explanation formula. In: Proceedings of the AAAI conference on artificial intelligence, vol 32","DOI":"10.1609\/aaai.v32i1.11564"},{"key":"6388_CR21","unstructured":"Zhang J, Bareinboim E (2018) Equality of opportunity in classification: a causal approach. Adv Neural Inf Process Syst 31"},{"key":"6388_CR22","doi-asserted-by":"crossref","unstructured":"Huang W, Zhang L, Wu X (2022) Achieving counterfactual fairness for causal bandit. In: Proceedings of the AAAI conference on artificial intelligence, vol 36, pp 6952\u20136959","DOI":"10.1609\/aaai.v36i6.20653"},{"key":"6388_CR23","first-page":"1854","volume":"33","author":"Z Wang","year":"2020","unstructured":"Wang Z, Chen X, Wen R, Huang S-L, Kuruoglu E, Zheng Y (2020) Information theoretic counterfactual learning from missing-not-at-random feedback. Adv Neural Inf Process Syst 33:1854\u20131864","journal-title":"Adv Neural Inf Process Syst"},{"issue":"20","key":"6388_CR24","doi-asserted-by":"publisher","first-page":"18097","DOI":"10.1007\/s00521-022-07373-4","volume":"34","author":"H Liu","year":"2022","unstructured":"Liu H, Wang Y, Lin H, Xu B, Zhao N (2022) Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems. Neural Comput Appl 34(20):18097\u201318111","journal-title":"Neural Comput Appl"},{"key":"6388_CR25","doi-asserted-by":"crossref","unstructured":"Zhu Z, Kim J, Nguyen T, Fenton A, Caverlee J (2021) Fairness among new items in cold start recommender systems. In: Proceedings of the 44th International ACM SIGIR conference on research and development in information retrieval, pp 767\u2013776","DOI":"10.1145\/3404835.3462948"},{"key":"6388_CR26","doi-asserted-by":"crossref","unstructured":"Geyik SC, Ambler S, Kenthapadi K (2019) Fairness-aware ranking in search & recommendation systems with application to linkedin talent search. In: Proceedings of the 25th Acm Sigkdd international conference on knowledge discovery & data mining, pp 2221\u20132231","DOI":"10.1145\/3292500.3330691"},{"issue":"1","key":"6388_CR27","first-page":"1","volume":"17","author":"Q Li","year":"2023","unstructured":"Li Q, Wang X, Wang Z, Xu G (2023) Be causal: de-biasing social network confounding in recommendation. ACM Trans Knowl Disc Data 17(1):1\u201323","journal-title":"ACM Trans Knowl Disc Data"},{"issue":"5","key":"6388_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3444944","volume":"15","author":"L Yao","year":"2021","unstructured":"Yao L, Chu Z, Li S, Li Y, Gao J, Zhang A (2021) A survey on causal inference. ACM Trans Knowl Disc Data (TKDD) 15(5):1\u201346","journal-title":"ACM Trans Knowl Disc Data (TKDD)"},{"key":"6388_CR29","doi-asserted-by":"crossref","unstructured":"Wang T, Huang J, Zhang H, Sun Q (2020) Visual commonsense representation learning via causal inference. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp 378\u2013379","DOI":"10.1109\/CVPRW50498.2020.00197"},{"key":"6388_CR30","doi-asserted-by":"crossref","unstructured":"Agarwal V, Shetty R, Fritz M (2020) Towards causal vqa: revealing and reducing spurious correlations by invariant and covariant semantic editing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9690\u20139698","DOI":"10.1109\/CVPR42600.2020.00971"},{"key":"6388_CR31","doi-asserted-by":"crossref","unstructured":"Tan C, Lee L, Pang B (2014) The effect of wording on message propagation: topic-and author-controlled natural experiments on twitter. arXiv:1405.1438","DOI":"10.3115\/v1\/P14-1017"},{"key":"6388_CR32","unstructured":"Egami N, Fong CJ, Grimmer J, Roberts ME, Stewart BM (2018) How to make causal inferences using texts. arXiv:1802.02163"},{"key":"6388_CR33","doi-asserted-by":"crossref","unstructured":"Lada A, Peysakhovich A, Aparicio D, Bailey M (2019) Observational data for heterogeneous treatment effects with application to recommender systems. In: Proceedings of the 2019 ACM conference on economics and computation, pp 199\u2013213","DOI":"10.1145\/3328526.3329558"},{"key":"6388_CR34","unstructured":"Schnabel T, Swaminathan A, Singh A, Chandak N, Joachims T (2016) Recommendations as treatments: debiasing learning and evaluation. In: International conference on machine learning. PMLR, pp 1670\u20131679"},{"key":"6388_CR35","doi-asserted-by":"crossref","unstructured":"Wang Y, Liang D, Charlin L, Blei DM (2020) Causal inference for recommender systems. In: Proceedings of the 14th ACM conference on recommender systems, pp 426\u2013431","DOI":"10.1145\/3383313.3412225"},{"key":"6388_CR36","doi-asserted-by":"crossref","unstructured":"Wang W, Zhang Y, Li H, Wu P, Feng F, He X (2023) Causal recommendation: progresses and future directions. In: Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval, pp 3432\u20133435","DOI":"10.1145\/3539618.3594245"},{"key":"6388_CR37","doi-asserted-by":"crossref","unstructured":"Christakopoulou K, Traverse M, Potter T, Marriott E, Li D, Haulk C, Chi EH, Chen M (2020) Deconfounding user satisfaction estimation from response rate bias. In: Proceedings of the 14th ACM conference on recommender systems, pp 450\u2013455","DOI":"10.1145\/3383313.3412208"},{"key":"6388_CR38","doi-asserted-by":"crossref","unstructured":"Chang B, Jang G, Kim S, Kang J (2020) Learning graph-based geographical latent representation for point-of-interest recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 135\u2013144","DOI":"10.1145\/3340531.3411905"},{"issue":"4","key":"6388_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3190616","volume":"51","author":"M Quadrana","year":"2018","unstructured":"Quadrana M, Cremonesi P, Jannach D (2018) Sequence-aware recommender systems. ACM Comput Surv (CSUR) 51(4):1\u201336","journal-title":"ACM Comput Surv (CSUR)"},{"key":"6388_CR40","doi-asserted-by":"crossref","unstructured":"Lim N, Hooi B, Ng S-K, Wang X, Goh YL, Weng R, Varadarajan J (2020) Stp-udgat: spatial-temporal-preference user dimensional graph attention network for next poi recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 845\u2013854","DOI":"10.1145\/3340531.3411876"},{"issue":"1","key":"6388_CR41","first-page":"1","volume":"42","author":"D Wang","year":"2023","unstructured":"Wang D, Zhang X, Yin Y, Yu D, Xu G, Deng S (2023) Multi-view enhanced graph attention network for session-based music recommendation. ACM Transactions on Information Systems 42(1):1\u201330","journal-title":"ACM Transactions on Information Systems"},{"key":"6388_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126734","volume":"557","author":"X Wang","year":"2023","unstructured":"Wang X, Wang D, Yu D, Wu R, Yang Q, Deng S, Xu G (2023) Intent-aware graph neural network for point-of-interest embedding and recommendation. Neurocomputing 557:126734","journal-title":"Neurocomputing"},{"issue":"4","key":"6388_CR43","doi-asserted-by":"publisher","first-page":"4367","DOI":"10.1109\/TNNLS.2024.3371592","volume":"35","author":"M Li","year":"2024","unstructured":"Li M, Micheli A, Wang YG, Pan S, Li\u00f3 P, Gnecco GS, Sanguineti M (2024) Guest editorial: deep neural networks for graphs: theory, models, algorithms, and applications. IEEE Transactions on Neural Networks and Learning Systems 35(4):4367\u20134372","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"6388_CR44","doi-asserted-by":"publisher","first-page":"102428","DOI":"10.1016\/j.inffus.2024.102428","volume":"109","author":"M Li","year":"2024","unstructured":"Li M, Zhou S, Chen Y, Huang C, Jiang Y (2024) Educross: dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides. Inf Fusion 109:102428","journal-title":"Inf Fusion"},{"key":"6388_CR45","doi-asserted-by":"crossref","unstructured":"Li J, Zheng R, Feng H, Li M, Zhuang X (2024) Permutation equivariant graph framelets for heterophilous graph learning. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2024.3370918"},{"key":"6388_CR46","doi-asserted-by":"publisher","first-page":"109874","DOI":"10.1016\/j.patcog.2023.109874","volume":"144","author":"M Li","year":"2023","unstructured":"Li M, Zhang L, Cui L, Bai L, Li Z, Wu X (2023) Blog: bootstrapped graph representation learning with local and global regularization for recommendation. Pattern Recognit 144:109874","journal-title":"Pattern Recognit"},{"key":"6388_CR47","unstructured":"Boratto L, Fabbri F, Fenu G, Marras M, Medda G (2023) Explaining unfairness in gnn-based recommendation. In: The second learning on graphs conference"},{"key":"6388_CR48","doi-asserted-by":"crossref","unstructured":"Huang C, Wang Y, Jiang Y, Li M, Huang X, Wang S, Pan S, Zhou C (2024) Flow2gnn: flexible two-way flow message passing for enhancing gnns beyond homophily. IEEE Trans Cybern","DOI":"10.1109\/TCYB.2024.3412149"},{"key":"6388_CR49","doi-asserted-by":"crossref","unstructured":"Dong Y, Liu N, Jalaian B, Li J (2022) Edits: modeling and mitigating data bias for graph neural networks. In: Proceedings of the ACM web conference 2022, pp 1259\u20131269","DOI":"10.1145\/3485447.3512173"},{"key":"6388_CR50","unstructured":"Gupta P, Garg D, Malhotra P, Vig L, Shroff GM (2019) Niser: normalized item and session representations with graph neural networks. arXiv:1909.04276"},{"key":"6388_CR51","unstructured":"Bose A, Hamilton W (2019) Compositional fairness constraints for graph embeddings. In: International conference on machine learning. PMLR, pp 715\u2013724"},{"key":"6388_CR52","doi-asserted-by":"crossref","unstructured":"Rahman T, Surma B, Backes M, Zhang Y (2019) Fairwalk: towards fair graph embedding","DOI":"10.24963\/ijcai.2019\/456"},{"key":"6388_CR53","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval, pp 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"issue":"8","key":"6388_CR54","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":"6388_CR55","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv:1609.02907"},{"key":"6388_CR56","unstructured":"Dong Y, Lizardo O, Chawla NV (2016) Do the young live in a \u201csmaller world\u201d than the old? age-specific degrees of separation in a large-scale mobile communication network. arXiv:1606.07556"},{"issue":"6","key":"6388_CR57","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1109\/TKDE.2005.99","volume":"17","author":"G Adomavicius","year":"2005","unstructured":"Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734\u2013749","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6388_CR58","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst 30"},{"key":"6388_CR59","doi-asserted-by":"crossref","unstructured":"Wu L, Chen L, Shao P, Hong R, Wang X, Wang M (2021) Learning fair representations for recommendation: a graph-based perspective. In: Proceedings of the web conference 2021, pp 2198\u20132208","DOI":"10.1145\/3442381.3450015"},{"issue":"11","key":"6388_CR60","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"key":"6388_CR61","doi-asserted-by":"crossref","unstructured":"Wei T, He J (2022) Comprehensive fair meta-learned recommender system. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 1989\u20131999","DOI":"10.1145\/3534678.3539269"},{"key":"6388_CR62","doi-asserted-by":"crossref","unstructured":"Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214\u2013226","DOI":"10.1145\/2090236.2090255"},{"key":"6388_CR63","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/s11222-017-9746-6","volume":"28","author":"D Hand","year":"2018","unstructured":"Hand D, Christen P (2018) A note on using the f-measure for evaluating record linkage algorithms. Stat Comput 28:539\u2013547","journal-title":"Stat Comput"},{"key":"6388_CR64","doi-asserted-by":"crossref","unstructured":"Li Y, Chen H, Xu S, Ge Y, Zhang Y (2021) Towards personalized fairness based on causal notion. In: Proceedings of the 44th International ACM SIGIR conference on research and development in information retrieval, pp 1054\u20131063","DOI":"10.1145\/3404835.3462966"},{"key":"6388_CR65","doi-asserted-by":"crossref","unstructured":"Zhang X, Shi T, Xu J, Dong Z, Wen J-R (2024) Model-agnostic causal embedding learning for counterfactually group-fair recommendation. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2024.3424906"},{"key":"6388_CR66","doi-asserted-by":"crossref","unstructured":"Liu S, Wu G, Deng X, Lu H, Wang B, Yang L, Park JJ (2023) Graph sampling based fairness-aware recommendation over sensitive attribute removal. In: 2023 IEEE International conference on data mining (ICDM). IEEE, pp 428\u2013437","DOI":"10.1109\/ICDM58522.2023.00052"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06388-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06388-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06388-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:31:21Z","timestamp":1758310281000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06388-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":66,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6388"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06388-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,2,25]]},"assertion":[{"value":"16 February 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This statement is to certify that all authors have seen and approved the manuscript being submitted. We warrant that we have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"475"}}