{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T03:39:22Z","timestamp":1772163562936,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T00:00:00Z","timestamp":1735516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s40747-024-01758-9","type":"journal-article","created":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T19:47:51Z","timestamp":1735588071000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley value"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2319-3824","authenticated-orcid":false,"given":"Weiwei","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6569-3029","authenticated-orcid":false,"given":"Wenping","family":"Ma","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3619-8966","authenticated-orcid":false,"given":"Kun","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,30]]},"reference":[{"key":"1758_CR1","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.knosys.2015.03.001","volume":"82","author":"BK Patra","year":"2015","unstructured":"Patra BK, Launonen R, Ollikainen V, Nandi S (2015) A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowl-Based Syst 82:163\u2013177. https:\/\/doi.org\/10.1016\/j.knosys.2015.03.001","journal-title":"Knowl-Based Syst"},{"key":"1758_CR2","doi-asserted-by":"publisher","DOI":"10.13052\/jwe1540-9589.20210","author":"S Feng","year":"2021","unstructured":"Feng S, Meng J, Zhang J (2021) News recommendation systems in the era of information overload. J Web Eng. https:\/\/doi.org\/10.13052\/jwe1540-9589.20210","journal-title":"J Web Eng"},{"issue":"1","key":"1758_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2012.02.006","volume":"519","author":"L L\u00fc","year":"2012","unstructured":"L\u00fc L, Medo M, Yeung CH, Zhang Y-C, Zhang Z-K, Zhou T (2012) Recommender systems. Phys Rep 519(1):1\u201349. https:\/\/doi.org\/10.1016\/j.physrep.2012.02.006","journal-title":"Phys Rep"},{"issue":"4","key":"1758_CR4","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1109\/TSC.2020.2964552","volume":"13","author":"Z Cui","year":"2020","unstructured":"Cui Z, Xu X, Fei X, Cai X, Cao Y, Zhang W, Chen J (2020) Personalized recommendation system based on collaborative filtering for iot scenarios. IEEE Trans Serv Comput 13(4):685\u2013695. https:\/\/doi.org\/10.1109\/TSC.2020.2964552","journal-title":"IEEE Trans Serv Comput"},{"issue":"6","key":"1758_CR5","doi-asserted-by":"publisher","first-page":"3796","DOI":"10.1109\/TSMC.2019.2931723","volume":"51","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Yin C, Wu Q, He Q, Zhu H (2019) Location-aware deep collaborative filtering for service recommendation. IEEE Trans Syst Man Cybern Syst 51(6):3796\u20133807. https:\/\/doi.org\/10.1109\/TSMC.2019.2931723","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"1758_CR6","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":"1758_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-024-01344-z","author":"Q Huang","year":"2024","unstructured":"Huang Q, Zeng Y (2024) Improving academic performance predictions with dual graph neural networks. Complex Intelli Syst. https:\/\/doi.org\/10.1007\/s40747-024-01344-z","journal-title":"Complex Intelli Syst"},{"key":"1758_CR8","doi-asserted-by":"publisher","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":"1758_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.entcom.2024.100715","volume":"51","author":"N Li","year":"2024","unstructured":"Li N, Xia Y (2024) Movie recommendation based on als collaborative filtering recommendation algorithm with deep learning model. Entertain Comput 51:100715. https:\/\/doi.org\/10.1016\/j.entcom.2024.100715","journal-title":"Entertain Comput"},{"issue":"3","key":"1758_CR10","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1109\/TCYB.2018.2795041","volume":"49","author":"M Fu","year":"2018","unstructured":"Fu M, Qu H, Yi Z, Lu L, Liu Y (2018) A novel deep learning-based collaborative filtering model for recommendation system. IEEE Trans Cybern 49(3):1084\u20131096. https:\/\/doi.org\/10.1109\/TCYB.2018.2795041","journal-title":"IEEE Trans Cybern"},{"key":"1758_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61185\/SMIJ.2023.22104","volume":"2","author":"J Alenizi","year":"2023","unstructured":"Alenizi J, Alrashdi I (2023) Sfmr-sh: secure framework for mitigating ransomware attacks in smart healthcare using blockchain technology. Sustain Mach Intell J 2:1\u20134. https:\/\/doi.org\/10.61185\/SMIJ.2023.22104","journal-title":"Sustain Mach Intell J"},{"key":"1758_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61185\/SMIJ.2023.33106","volume":"3","author":"M Ismail","year":"2023","unstructured":"Ismail M, Abd El-Gawad AF (2023) Revisiting zero-trust security for internet of things. Sustain Mach Intell J 3:1\u20136. https:\/\/doi.org\/10.61185\/SMIJ.2023.33106","journal-title":"Sustain Mach Intell J"},{"issue":"3\u20134","key":"1758_CR13","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/0400000042","volume":"9","author":"C Dwork","year":"2014","unstructured":"Dwork C, Roth A (2014) The algorithmic foundations of differential privacy. Found Trends Theoret Comput Sci 9(3\u20134):211\u2013407. https:\/\/doi.org\/10.1561\/0400000042","journal-title":"Found Trends Theoret Comput Sci"},{"key":"1758_CR14","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.ins.2019.06.021","volume":"502","author":"T Guo","year":"2019","unstructured":"Guo T, Luo J, Dong K, Yang M (2019) Locally differentially private item-based collaborative filtering. Inf Sci 502:229\u2013246. https:\/\/doi.org\/10.1016\/j.ins.2019.06.021","journal-title":"Inf Sci"},{"key":"1758_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119002","author":"T Guo","year":"2023","unstructured":"Guo T, Peng S, Li Y, Zhou M, Truong T-K (2023) Community-based social recommendation under local differential privacy protection. Inform Sci. https:\/\/doi.org\/10.1016\/j.ins.2023.119002","journal-title":"Inform Sci"},{"key":"1758_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114366","volume":"168","author":"Z Chen","year":"2021","unstructured":"Chen Z, Wang Y, Zhang S, Zhong H, Chen L (2021) Differentially private user-based collaborative filtering recommendation based on k-means clustering. Expert Syst Appl 168:114366. https:\/\/doi.org\/10.1016\/j.eswa.2020.114366","journal-title":"Expert Syst Appl"},{"key":"1758_CR17","doi-asserted-by":"publisher","unstructured":"Zhu X, Sun Y (2013) Differential Privacy for Collaborative Filtering Recommender Algorithm. In: Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics. IWSPA \u201916, pp. 9\u201316. Association for Computing Machinery, New York, NY, USA . https:\/\/doi.org\/10.1145\/2875475.2875483","DOI":"10.1145\/2875475.2875483"},{"key":"1758_CR18","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.measurement.2016.05.058","volume":"91","author":"H Koohi","year":"2016","unstructured":"Koohi H, Kiani K (2016) User based collaborative filtering using fuzzy c-means. Measurement 91:134\u2013139. https:\/\/doi.org\/10.1016\/j.measurement.2016.05.058","journal-title":"Measurement"},{"issue":"1","key":"1758_CR19","doi-asserted-by":"publisher","first-page":"309","DOI":"10.3233\/JIFS-212216","volume":"43","author":"Y Zhong","year":"2022","unstructured":"Zhong Y, Huang C, Li Q (2022) A collaborative filtering recommendation algorithm based on fuzzy c-means clustering. J Intell Fuzzy Syst 43(1):309\u2013323. https:\/\/doi.org\/10.3233\/JIFS-212216","journal-title":"J Intell Fuzzy Syst"},{"key":"1758_CR20","doi-asserted-by":"publisher","first-page":"2399","DOI":"10.3837\/tiis.2021.07.006","volume":"15","author":"L Duan","year":"2021","unstructured":"Duan L, Wang W, Han B (2021) A hybrid recommendation system based on fuzzy c-means clustering and supervised learning. Korean Soc Internet Inform (KSII) 15:2399\u20132413. https:\/\/doi.org\/10.3837\/tiis.2021.07.006","journal-title":"Korean Soc Internet Inform (KSII)"},{"key":"1758_CR21","doi-asserted-by":"publisher","unstructured":"Liu J, Kang X, Nishide S, Ren F (2020) Collaborative Filtering Recommendation Algorithm Based on Bisecting K-means Clustering. In: International Symposium on Artificial Intelligence and Robotics 2020, vol. 11574, pp. 311\u2013318 . https:\/\/doi.org\/10.1117\/12.2580026. SPIE","DOI":"10.1117\/12.2580026"},{"key":"1758_CR22","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1903\/1\/012036","volume":"1903","author":"L Chen","year":"2021","unstructured":"Chen L, Luo Y, Liu X, Wang W, Ni M (2021) Improved collaborative filtering recommendation algorithm based on user attributes and k-means clustering algorithm. J Phys Conf Ser 1903:012036. https:\/\/doi.org\/10.1088\/1742-6596\/1903\/1\/012036","journal-title":"J Phys Conf Ser"},{"key":"1758_CR23","doi-asserted-by":"publisher","unstructured":"Zarzour H, Maazouzi F, Al-Zinati M, Nusayr A, Alsmirat M, Al-Ayyoub M, Jararweh Y (2022) Using k-means clustering ensemble to improve the performance in recommender systems. In: 2022 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), pp. 176\u2013180 . https:\/\/doi.org\/10.1109\/IDSTA55301.2022.9923070. IEEE","DOI":"10.1109\/IDSTA55301.2022.9923070"},{"key":"1758_CR24","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.knosys.2019.03.009","volume":"175","author":"J Deng","year":"2019","unstructured":"Deng J, Guo J, Wang Y (2019) A novel k-medoids clustering recommendation algorithm based on probability distribution for collaborative filtering. Knowl-Based Syst 175:96\u2013106. https:\/\/doi.org\/10.1016\/j.knosys.2019.03.009","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"1758_CR25","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/s40747-019-00123-5","volume":"6","author":"J Chen","year":"2020","unstructured":"Chen J, Zhao C, Chen L (2020) Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering. Complex Intell Syst 6(1):147\u2013156. https:\/\/doi.org\/10.1007\/s40747-019-00123-5","journal-title":"Complex Intell Syst"},{"key":"1758_CR26","doi-asserted-by":"publisher","first-page":"8279","DOI":"10.1007\/s00521-018-3959-2","volume":"31","author":"M Jiang","year":"2019","unstructured":"Jiang M, Zhang Z, Jiang J, Wang Q, Pei Z (2019) A collaborative filtering recommendation algorithm based on information theory and bi-clustering. Neural Comput Appl 31:8279\u20138287. https:\/\/doi.org\/10.1007\/s00521-018-3959-2","journal-title":"Neural Comput Appl"},{"key":"1758_CR27","doi-asserted-by":"publisher","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. Phys A 561:125140. https:\/\/doi.org\/10.1016\/j.physa.2020.125140","journal-title":"Phys A"},{"issue":"3","key":"1758_CR28","doi-asserted-by":"publisher","first-page":"2961","DOI":"10.1109\/TKDE.2021.3115776","volume":"35","author":"S Jumonji","year":"2023","unstructured":"Jumonji S, Sakai K, Sun M-T, Ku W-S (2023) Privacy-preserving collaborative filtering using fully homomorphic encryption. IEEE Trans Knowl Data Eng 35(3):2961\u20132974. https:\/\/doi.org\/10.1109\/TKDE.2021.3115776","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1758_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3212509","volume":"21","author":"J Kim","year":"2018","unstructured":"Kim J, Koo D, Kim Y, Yoon H, Shin J, Kim S (2018) Efficient privacy-preserving matrix factorization for recommendation via fully homomorphic encryption. ACM Trans Privacy Secur (TOPS) 21(4):1\u201330. https:\/\/doi.org\/10.1145\/3212509","journal-title":"ACM Trans Privacy Secur (TOPS)"},{"key":"1758_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2023.3243598","author":"J Zhou","year":"2023","unstructured":"Zhou J, Gao G, Cao Z, Choo K-KR, Dong X (2023) Lightweight privacy-preserving distributed recommender system using tag-based multikey fully homomorphic data encapsulation. IEEE Trans Dependable Secure Comput. https:\/\/doi.org\/10.1109\/TDSC.2023.3243598","journal-title":"IEEE Trans Dependable Secure Comput"},{"key":"1758_CR31","unstructured":"Ren H, Xu G, Zhang T, Ning J, Huang X, Li H, Lu R (2022) Efficiency boosting of secure cross-platform recommender systems over sparse data. arXiv preprint arXiv:2212.01537"},{"key":"1758_CR32","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.future.2013.07.019","volume":"36","author":"T Zhu","year":"2014","unstructured":"Zhu T, Ren Y, Zhou W, Rong J, Xiong P (2014) An effective privacy preserving algorithm for neighborhood-based collaborative filtering. Futur Gener Comput Syst 36:142\u2013155. https:\/\/doi.org\/10.1016\/j.future.2013.07.019","journal-title":"Futur Gener Comput Syst"},{"key":"1758_CR33","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.future.2018.05.077","volume":"109","author":"P Xiong","year":"2020","unstructured":"Xiong P, Zhang L, Zhu T, Li G, Zhou W (2020) Private collaborative filtering under untrusted recommender server. Futur Gener Comput Syst 109:511\u2013520. https:\/\/doi.org\/10.1016\/j.future.2018.05.077","journal-title":"Futur Gener Comput Syst"},{"key":"1758_CR34","doi-asserted-by":"publisher","DOI":"10.1109\/OJCS.2024.3396344","author":"C Chronis","year":"2024","unstructured":"Chronis C, Varlamis I, Himeur Y, Sayed AN, Al-Hasan TM, Nhlabatsi A, Bensaali F, Dimitrakopoulos G (2024) A survey on the use of federated learning in privacy-preserving recommender systems. IEEE Open J Comput Soc. https:\/\/doi.org\/10.1109\/OJCS.2024.3396344","journal-title":"IEEE Open J Comput Soc"},{"key":"1758_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3411402","author":"C Feng","year":"2024","unstructured":"Feng C, Feng D, Huang G, Liu Z, Wang Z, Xia X-G (2024) Robust privacy-preserving recommendation systems driven by multimodal federated learning. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2024.3411402","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"1758_CR36","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s41060-023-00442-4","volume":"17","author":"M Harasic","year":"2024","unstructured":"Harasic M, Keese F-S, Mattern D, Paschke A (2024) Recent advances and future challenges in federated recommender systems. Int J Data Sci Anal 17(4):337\u2013357. https:\/\/doi.org\/10.1007\/s41060-023-00442-4","journal-title":"Int J Data Sci Anal"},{"key":"1758_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61185\/SMIJ.2023.22105","volume":"2","author":"AA Metwaly","year":"2023","unstructured":"Metwaly AA, Elhenawy I (2023) Protecting iot devices from botnet threats: a federated machine learning solution. Sustain Mach Intell J 2:1\u20135. https:\/\/doi.org\/10.61185\/SMIJ.2023.22105","journal-title":"Sustain Mach Intell J"},{"key":"1758_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61185\/SMIJ.2023.44104","volume":"4","author":"AA Metwaly","year":"2023","unstructured":"Metwaly AA, Elhenawy I (2023) Sustainable intrusion detection in vehicular controller area networks using machine intelligence paradigm. Sustain Mach Intell J 4:1\u20134. https:\/\/doi.org\/10.61185\/SMIJ.2023.44104","journal-title":"Sustain Mach Intell J"},{"key":"1758_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.61356\/SMIJ.2024.77103","volume":"7","author":"SA Walli","year":"2024","unstructured":"Walli SA, Sallam K (2024) Machine learning for intrusion detection: a reproducible baseline is all you need. Sustain Mach Intell J 7:1\u20133. https:\/\/doi.org\/10.61356\/SMIJ.2024.77103","journal-title":"Sustain Mach Intell J"},{"key":"1758_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.003.2400038","author":"K Yan","year":"2024","unstructured":"Yan K, Ma W, Sun S (2024) Communications and networks resources sharing in 6g: challenges, architecture, and opportunities. IEEE Wirel Commun. https:\/\/doi.org\/10.1109\/MWC.003.2400038","journal-title":"IEEE Wirel Commun"},{"key":"1758_CR41","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.eswa.2017.04.027","volume":"83","author":"H Koohi","year":"2017","unstructured":"Koohi H, Kiani K (2017) A new method to find neighbor users that improves the performance of collaborative filtering. Expert Syst Appl 83:30\u201339. https:\/\/doi.org\/10.1016\/j.eswa.2017.04.027","journal-title":"Expert Syst Appl"},{"key":"1758_CR42","doi-asserted-by":"publisher","unstructured":"Fkih F (2022) Similarity measures for collaborative filtering-based recommender systems: review and experimental comparison. J King Saud Univ-Comput Inform Sci 34(9):7645\u20137669. https:\/\/doi.org\/10.1016\/j.jksuci.2021.09.014","DOI":"10.1016\/j.jksuci.2021.09.014"},{"key":"1758_CR43","doi-asserted-by":"publisher","unstructured":"Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. Comput Geosci 10(2):191\u2013203. https:\/\/doi.org\/10.1016\/0098-3004(84)90020-7","DOI":"10.1016\/0098-3004(84)90020-7"},{"key":"1758_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113856","volume":"165","author":"S Askari","year":"2021","unstructured":"Askari S (2021) Fuzzy c-means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: Review and development. Expert Syst Appl 165:113856. https:\/\/doi.org\/10.1016\/j.eswa.2020.113856","journal-title":"Expert Syst Appl"},{"issue":"4","key":"1758_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-031-02350-7","volume":"8","author":"N Li","year":"2016","unstructured":"Li N, Lyu M, Su D, Yang W (2016) Differential privacy: from theory to practice. Synth Lect Inform Secur Privacy Trust 8(4):1\u2013138. https:\/\/doi.org\/10.1007\/978-3-031-02350-7","journal-title":"Synth Lect Inform Secur Privacy Trust"},{"key":"1758_CR46","unstructured":"Roger BM, et al. (1991) Game theory: analysis of conflict. The President and Fellows of Harvard College, USA 66"},{"issue":"5","key":"1758_CR47","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1109\/TKDE.2012.73","volume":"25","author":"VK Garg","year":"2012","unstructured":"Garg VK, Narahari Y, Murty MN (2012) Novel biobjective clustering (bigc) based on cooperative game theory. IEEE Trans Knowl Data Eng 25(5):1070\u20131082. https:\/\/doi.org\/10.1109\/TKDE.2012.73","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1758_CR48","doi-asserted-by":"publisher","unstructured":"Genther H, Runkler TA, Glesner M (1994) Defuzzification based on fuzzy clustering. Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference 3, 1645\u20131648 . https:\/\/doi.org\/10.1109\/FUZZY.1994.343943","DOI":"10.1109\/FUZZY.1994.343943"},{"key":"1758_CR49","doi-asserted-by":"publisher","unstructured":"Ji H, Li J, Ren C, He M (2013) Hybrid collaborative filtering model for improved recommendation. In: Proceedings of 2013 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 142\u2013145 . https:\/\/doi.org\/10.1109\/SOLI.2013.6611398. IEEE","DOI":"10.1109\/SOLI.2013.6611398"},{"key":"1758_CR50","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.ins.2017.08.008","volume":"418","author":"Y Wang","year":"2017","unstructured":"Wang Y, Deng J, Gao J, Zhang P (2017) A hybrid user similarity model for collaborative filtering. Inf Sci 418:102\u2013118. https:\/\/doi.org\/10.1016\/j.ins.2017.08.008","journal-title":"Inf Sci"}],"container-title":["Complex & Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01758-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-024-01758-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-024-01758-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T20:20:54Z","timestamp":1738268454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-024-01758-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,30]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1758"],"URL":"https:\/\/doi.org\/10.1007\/s40747-024-01758-9","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,30]]},"assertion":[{"value":"23 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 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 Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"107"}}