{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:02:44Z","timestamp":1775815364193,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62476078"],"award-info":[{"award-number":["No.62476078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Project of Natural Science Foundation of Hebei Province","award":["No.F2023205006"],"award-info":[{"award-number":["No.F2023205006"]}]},{"name":"the Key Development Fund of Hebei Normal University","award":["No.L2024ZD06"],"award-info":[{"award-number":["No.L2024ZD06"]}]},{"name":"the Graduate Innovation Project of the School of Mathematical Sciences, Hebei Normal University","award":["ycxzzbs202606"],"award-info":[{"award-number":["ycxzzbs202606"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s10489-026-07224-y","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T09:23:14Z","timestamp":1775812994000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-granularity rough k-means clustering in the optimization of collaborative filtering algorithms"],"prefix":"10.1007","volume":"56","author":[{"given":"Ziru","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3753-4490","authenticated-orcid":false,"given":"Jusheng","family":"Mi","sequence":"additional","affiliation":[]},{"given":"Ziyun","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"7224_CR1","doi-asserted-by":"publisher","first-page":"106194","DOI":"10.1016\/j.engappai.2023.106194","volume":"123","author":"V Felizardo","year":"2023","unstructured":"Felizardo V, Garcia NM, Megdiche I, Pombo N, Sousa M, Babi\u010d F (2023) Hypoglycaemia prediction using information fusion and classifiers consensus. Eng Appl Artif Intell 123:106194. https:\/\/doi.org\/10.1016\/j.engappai.2023.106194","journal-title":"Eng Appl Artif Intell"},{"key":"7224_CR2","doi-asserted-by":"publisher","first-page":"101485","DOI":"10.1016\/j.aei.2021.101485","volume":"51","author":"HC Yan","year":"2022","unstructured":"Yan HC, Wang ZR, Niu JY, Xue T (2022) Application of covering rough granular computing model in collaborative filtering recommendation algorithm optimization. Adv Eng Inform 51:101485. https:\/\/doi.org\/10.1016\/j.aei.2021.101485","journal-title":"Adv Eng Inform"},{"key":"7224_CR3","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/TASE.2014.2348555","volume":"13","author":"X Luo","year":"2016","unstructured":"Luo X, Zhou M, Leung H, Xia Y, Zhu Q, You Z, Li S (2016) An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering. IEEE Trans Autom Sci Eng 13:333\u2013343. https:\/\/doi.org\/10.1109\/TASE.2014.2348555","journal-title":"IEEE Trans Autom Sci Eng"},{"key":"7224_CR4","doi-asserted-by":"publisher","first-page":"4591","DOI":"10.1109\/TII.2019.2893714","volume":"15","author":"B Yi","year":"2019","unstructured":"Yi B, Shen X, Liu H, Zhang Z, Zhang W, Liu S, Xiong N (2019) Deep Matrix Factorization With Implicit Feedback Embedding for Recommendation System. IEEE Trans Industr Inf 15:4591\u20134601. https:\/\/doi.org\/10.1109\/TII.2019.2893714","journal-title":"IEEE Trans Industr Inf"},{"key":"7224_CR5","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.eij.2015.06.005","volume":"16","author":"FO Isinkaye","year":"2015","unstructured":"Isinkaye FO, Folajimi YO, Ojokoh BA (2015) Recommendation systems: Principles, methods and evaluation. Egyptian Inf J 16:261\u2013273. https:\/\/doi.org\/10.1016\/j.eij.2015.06.005","journal-title":"Egyptian Inf J"},{"key":"7224_CR6","doi-asserted-by":"publisher","first-page":"125140","DOI":"10.1016\/j.physa.2020.125140","volume":"561","author":"M Li","year":"2021","unstructured":"Li M, Wen L, Chen F (2021) A novel Collaborative Filtering recommendation approach based on Soft Co-Clustering. Physica A Stat Mechan Appl 561:125140. https:\/\/doi.org\/10.1016\/j.physa.2020.125140","journal-title":"Physica A Stat Mechan Appl"},{"key":"7224_CR7","doi-asserted-by":"publisher","first-page":"123700","DOI":"10.1016\/j.eswa.2024.123700","volume":"249","author":"J Guan","year":"2024","unstructured":"Guan J, Chen B, Yu S (2024) A hybrid similarity model for mitigating the cold-start problem of collaborative filtering in sparse data. Expert Syst Appl 249:123700. https:\/\/doi.org\/10.1016\/j.eswa.2024.123700","journal-title":"Expert Syst Appl"},{"key":"7224_CR8","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s40747-020-00212-w","volume":"7","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Lu J, Jin Y (2021) Artificial intelligence in recommender systems. Complex Intell Syst 7:439\u2013457. https:\/\/doi.org\/10.1007\/s40747-020-00212-w","journal-title":"Complex Intell Syst"},{"key":"7224_CR9","doi-asserted-by":"publisher","first-page":"117921","DOI":"10.1016\/j.eswa.2022.117921","volume":"207","author":"W Jiang","year":"2022","unstructured":"Jiang W, Luo J (2022) Graph neural network for traffic forecasting: A survey. Expert Syst Appl 207:117921. https:\/\/doi.org\/10.1016\/j.eswa.2022.117921","journal-title":"Expert Syst Appl"},{"key":"7224_CR10","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/s00607-015-0448-7","volume":"97","author":"A Abbas","year":"2015","unstructured":"Abbas A, Zhang L, Khan SU (2015) A survey on context-aware recommender systems based on computational intelligence techniques. Computing 97:667\u2013690. https:\/\/doi.org\/10.1007\/s00607-015-0448-7","journal-title":"Computing"},{"key":"7224_CR11","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 42:30\u201337. https:\/\/doi.org\/10.1109\/MC.2009.263","journal-title":"Computer"},{"key":"7224_CR12","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.neucom.2021.02.016","volume":"441","author":"C-S Wang","year":"2021","unstructured":"Wang C-S, Chen B-S, Chiang J-H (2021) TDD-BPR: The topic diversity discovering on Bayesian personalized ranking for personalized recommender system. Neurocomputing 441:202\u2013213. https:\/\/doi.org\/10.1016\/j.neucom.2021.02.016","journal-title":"Neurocomputing"},{"key":"7224_CR13","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neunet.2017.03.009","volume":"90","author":"F Zhuang","year":"2017","unstructured":"Zhuang F, Zhang Z, Qian M, Shi C, Xie X, He Q (2017) Representation learning via Dual-Autoencoder for recommendation. Neural Netw 90:83\u201389. https:\/\/doi.org\/10.1016\/j.neunet.2017.03.009","journal-title":"Neural Netw"},{"key":"7224_CR14","doi-asserted-by":"publisher","first-page":"102735","DOI":"10.1016\/j.jvcir.2019.102735","volume":"71","author":"K Wang","year":"2020","unstructured":"Wang K, Zhang T, Xue T, Lu Y, Na S-G (2020) E-commerce personalized recommendation analysis by deeply-learned clustering. J Vis Commun Image Represent 71:102735. https:\/\/doi.org\/10.1016\/j.jvcir.2019.102735","journal-title":"J Vis Commun Image Represent"},{"key":"7224_CR15","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.eswa.2017.09.058","volume":"92","author":"M Nilashi","year":"2018","unstructured":"Nilashi M, Ibrahim O, Bagherifard K (2018) A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems with Appications 92:507\u2013520. https:\/\/doi.org\/10.1016\/j.eswa.2017.09.058","journal-title":"Expert Systems with Appications"},{"key":"7224_CR16","doi-asserted-by":"publisher","first-page":"112138","DOI":"10.1016\/j.asoc.2024.112138","volume":"165","author":"T Niu","year":"2024","unstructured":"Niu T, Wang Z, Li W, Li K, Li Y, Xu G, Li B (2024) Learning trustworthy model from noisy labels based on rough set for surface defect detection. Appl Soft Comput 165:112138. https:\/\/doi.org\/10.1016\/j.asoc.2024.112138","journal-title":"Appl Soft Comput"},{"key":"7224_CR17","doi-asserted-by":"publisher","first-page":"9953","DOI":"10.1016\/j.jksuci.2021.12.021","volume":"34","author":"G Behera","year":"2022","unstructured":"Behera G, Nain N (2022) Handling data sparsity via item metadata embedding into deep collaborative recommender system. J King Saud Univ - Computer Inf Sci 34:9953\u20139963. https:\/\/doi.org\/10.1016\/j.jksuci.2021.12.021","journal-title":"J King Saud Univ - Computer Inf Sci"},{"key":"7224_CR18","doi-asserted-by":"publisher","first-page":"5775","DOI":"10.1109\/TNNLS.2021.3071392","volume":"33","author":"D Wu","year":"2022","unstructured":"Wu D, Shang M, Luo X, Wang Z (2022) An L$$_{1}$$ -and- L$$_{2}$$ -Norm-Oriented Latent Factor Model for Recommender Systems. IEEE Trans Neural Netw Learn Syst 33:5775\u20135788. https:\/\/doi.org\/10.1109\/TNNLS.2021.3071392","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7224_CR19","doi-asserted-by":"publisher","first-page":"121583","DOI":"10.1016\/j.eswa.2023.121583","volume":"237","author":"L Meng","year":"2024","unstructured":"Meng L, Liu Z, Chu D, Sheng QZ, Yu J, Song X (2024) POI recommendation for occasional groups Based on hybrid graph neural networks. Expert Syst Appl 237:121583. https:\/\/doi.org\/10.1016\/j.eswa.2023.121583","journal-title":"Expert Syst Appl"},{"key":"7224_CR20","doi-asserted-by":"publisher","first-page":"123575","DOI":"10.1016\/j.eswa.2024.123575","volume":"249","author":"S Fu","year":"2024","unstructured":"Fu S, Ren Q, Lv X, Li J (2024) Multi-behavior recommendation with SVD Graph Neural Networks. Expert Syst Appl 249:123575. https:\/\/doi.org\/10.1016\/j.eswa.2024.123575","journal-title":"Expert Syst Appl"},{"key":"7224_CR21","doi-asserted-by":"publisher","first-page":"123900","DOI":"10.1016\/j.eswa.2024.123900","volume":"250","author":"V Bhatia","year":"2024","unstructured":"Bhatia V (2024) DLSF: Deep learning and semantic fusion based recommendation system. Expert Syst Appl 250:123900. https:\/\/doi.org\/10.1016\/j.eswa.2024.123900","journal-title":"Expert Syst Appl"},{"key":"7224_CR22","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/BF01001956","volume":"11","author":"Z Pawlak","year":"1982","unstructured":"Pawlak Z (1982) Rough sets. Intern J Comput Inf Sci 11:341\u2013356. https:\/\/doi.org\/10.1007\/BF01001956","journal-title":"Intern J Comput Inf Sci"},{"key":"7224_CR23","doi-asserted-by":"publisher","unstructured":"Pawlak Z (1991). Rough Sets. https:\/\/doi.org\/10.1007\/978-94-011-3534-4","DOI":"10.1007\/978-94-011-3534-4"},{"key":"7224_CR24","doi-asserted-by":"publisher","first-page":"122390","DOI":"10.1016\/j.ins.2025.122390","volume":"718","author":"A Rachwa\u0142","year":"2025","unstructured":"Rachwa\u0142 A, Karczmarek P, Rachwa\u0142 A (2025) Rough set-inspired isolation forest. Inf Sci 718:122390. https:\/\/doi.org\/10.1016\/j.ins.2025.122390","journal-title":"Inf Sci"},{"key":"7224_CR25","doi-asserted-by":"publisher","first-page":"113344","DOI":"10.1016\/j.asoc.2025.113344","volume":"180","author":"J Luo","year":"2025","unstructured":"Luo J, Hu M, Shi C, Yao Y (2025) Three-way decision with granular rough sets. Appl Soft Comput 180:113344. https:\/\/doi.org\/10.1016\/j.asoc.2025.113344","journal-title":"Appl Soft Comput"},{"key":"7224_CR26","doi-asserted-by":"publisher","first-page":"109492","DOI":"10.1016\/j.ijar.2025.109492","volume":"186","author":"MA Khan","year":"2025","unstructured":"Khan MA (2025) Ranjan: A formal study of a rough set model integrating relational and neighbourhood system approaches. Int J Approximate Reason 186:109492. https:\/\/doi.org\/10.1016\/j.ijar.2025.109492","journal-title":"Int J Approximate Reason"},{"key":"7224_CR27","doi-asserted-by":"publisher","first-page":"122439","DOI":"10.1016\/j.ins.2025.122439","volume":"719","author":"G Fenza","year":"2025","unstructured":"Fenza G, Gaeta A, Loia V, Orciuoli F, Stanzione C (2025) Explaining vulnerabilities of biased news classifiers through rough sets and granular computing. Inf Sci 719:122439. https:\/\/doi.org\/10.1016\/j.ins.2025.122439","journal-title":"Inf Sci"},{"key":"7224_CR28","doi-asserted-by":"publisher","first-page":"122411","DOI":"10.1016\/j.ins.2025.122411","volume":"718","author":"W Ye","year":"2025","unstructured":"Ye W, Xu W (2025) Innovative multi-granularity granular-balls rough set for feature selection: Driving generalized multi-granularity rough set evolution with Zentropy integration. Inf Sci 718:122411. https:\/\/doi.org\/10.1016\/j.ins.2025.122411","journal-title":"Inf Sci"},{"key":"7224_CR29","doi-asserted-by":"publisher","first-page":"102844","DOI":"10.1016\/j.aei.2024.102844","volume":"62","author":"J Pei","year":"2024","unstructured":"Pei J, Yan P, Zhou H, Wu D, Chen J, Yi R (2024) A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm. Adv Eng Inform 62:102844. https:\/\/doi.org\/10.1016\/j.aei.2024.102844","journal-title":"Adv Eng Inform"},{"key":"7224_CR30","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.cmpb.2006.06.007","volume":"83","author":"X Wang","year":"2006","unstructured":"Wang X, Yang J, Jensen R, Liu X (2006) Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Comput Methods Programs Biomed 83:147\u2013156. https:\/\/doi.org\/10.1016\/j.cmpb.2006.06.007","journal-title":"Comput Methods Programs Biomed"},{"key":"7224_CR31","doi-asserted-by":"publisher","first-page":"24876","DOI":"10.1007\/s10489-023-04850-8","volume":"53","author":"T Wang","year":"2023","unstructured":"Wang T, Sun B, Jiang C (2023) Kernelized multi-granulation fuzzy rough set over hybrid attribute decision system and application to stroke risk prediction. Appl Intell 53:24876\u201324894. https:\/\/doi.org\/10.1007\/s10489-023-04850-8","journal-title":"Appl Intell"},{"key":"7224_CR32","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s10489-023-05194-z","volume":"54","author":"J Xu","year":"2024","unstructured":"Xu J, Zhou C, Xu S, Zhang L, Han Z (2024) Feature selection based on multi-perspective entropy of mixing uncertainty measure in variable-granularity rough set. Appl Intell 54:147\u2013168. https:\/\/doi.org\/10.1007\/s10489-023-05194-z","journal-title":"Appl Intell"},{"key":"7224_CR33","doi-asserted-by":"publisher","first-page":"102078","DOI":"10.1016\/j.ipm.2019.102078","volume":"57","author":"S Renjith","year":"2020","unstructured":"Renjith S, Sreekumar A, Jathavedan M (2020) An extensive study on the evolution of context-aware personalized travel recommender systems. Inform Process Manag 57:102078. https:\/\/doi.org\/10.1016\/j.ipm.2019.102078","journal-title":"Inform Process Manag"},{"key":"7224_CR34","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ins.2006.06.006","volume":"177","author":"Z Pawlak","year":"2007","unstructured":"Pawlak Z, Skowron A (2007) Rough sets: Some extensions. Inf Sci 177:28\u201340. https:\/\/doi.org\/10.1016\/j.ins.2006.06.006","journal-title":"Inf Sci"},{"key":"7224_CR35","doi-asserted-by":"publisher","first-page":"110268","DOI":"10.1016\/j.asoc.2023.110268","volume":"140","author":"A Das","year":"2023","unstructured":"Das A, Namtirtha A, Dutta A (2023) Levy-Cauchy arithmetic optimization algorithm combined with rough K-means for image segmentation. Appl Soft Comput 140:110268. https:\/\/doi.org\/10.1016\/j.asoc.2023.110268","journal-title":"Appl Soft Comput"},{"key":"7224_CR36","doi-asserted-by":"publisher","unstructured":"Zhang T, Ma F, Yue D, Peng C, O\u2019Hare G, M P (2020) Interval Type-2 Fuzzy Local Enhancement Based Rough K-Means Clustering Considering Imbalanced Clusters. IEEE Trans Fuzzy Syst 28:1925\u20131939. https:\/\/doi.org\/10.1109\/TFUZZ.2019.2924402","DOI":"10.1109\/TFUZZ.2019.2924402"},{"key":"7224_CR37","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1109\/TCYB.2023.3257274","volume":"54","author":"T Zhang","year":"2024","unstructured":"Zhang T, Zhang Y, Ma F, Peng C, Yue D, Pedrycz W (2024) Local Boundary Fuzzified Rough K-Means-Based Information Granulation Algorithm Under the Principle of Justifiable Granularity. IEEE Trans Cybern 54:519\u2013532. https:\/\/doi.org\/10.1109\/TCYB.2023.3257274","journal-title":"IEEE Trans Cybern"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07224-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-026-07224-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07224-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T09:23:16Z","timestamp":1775812996000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-026-07224-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["7224"],"URL":"https:\/\/doi.org\/10.1007\/s10489-026-07224-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"17 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2026","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 the following financial interests\/ personal relationships which may be considered as potential competing interests: Jusheng Mi reports financial support was provided by National Natural Science Foundation of China. Jusheng Mi reports a relationship with the Key Project of Natural Science Foundation of Hebei province. Ziru Wang was funded by the Graduate Innovation Project of the School of Mathematical Sciences, Hebei Normal University.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"201"}}