{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T18:05:00Z","timestamp":1775585100760,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T00:00:00Z","timestamp":1606089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"crossref","award":["PID2019-106493RB-I00 (DL-CEMG)"],"award-info":[{"award-number":["PID2019-106493RB-I00 (DL-CEMG)"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s00521-020-05494-2","type":"journal-article","created":{"date-parts":[[2020,11,23]],"date-time":"2020-11-23T15:37:53Z","timestamp":1606145873000},"page":"7291-7308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep learning feature selection to unhide demographic recommender systems factors"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0619-1322","authenticated-orcid":false,"given":"J.","family":"Bobadilla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2326-6752","authenticated-orcid":false,"given":"\u00c1.","family":"Gonz\u00e1lez-Prieto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4765-1479","authenticated-orcid":false,"given":"F.","family":"Ortega","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7959-1936","authenticated-orcid":false,"given":"R.","family":"Lara-Cabrera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"5494_CR1","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.knosys.2016.03.006","volume":"100","author":"MYH Al-Shamri","year":"2016","unstructured":"Al-Shamri MYH (2016) User profiling approaches for demographic recommender systems. Knowl Based Syst 100:175\u2013187","journal-title":"Knowl Based Syst"},{"key":"5494_CR2","doi-asserted-by":"publisher","first-page":"102053","DOI":"10.1016\/j.aquaeng.2020.102053","volume":"89","author":"A Banan","year":"2020","unstructured":"Banan A, Nasiri A, Taheri-Garavand A (2020) Deep learning-based appearance features extraction for automated carp species identification. Aquac Eng 89:102053","journal-title":"Aquac Eng"},{"issue":"22","key":"5494_CR3","doi-asserted-by":"publisher","first-page":"4290","DOI":"10.1016\/j.ins.2010.07.024","volume":"180","author":"AB Barrag\u00e1ns-Mart\u00ednez","year":"2010","unstructured":"Barrag\u00e1ns-Mart\u00ednez AB, Costa-Montenegro E, Burguillo JC, Rey-L\u00f3pez M, Mikic-Fonte FA, Peleteiro A (2010) A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inf Sci 180(22):4290\u20134311. https:\/\/doi.org\/10.1016\/j.ins.2010.07.024","journal-title":"Inf Sci"},{"issue":"1","key":"5494_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-018-9654-y","volume":"52","author":"Z Batmaz","year":"2019","unstructured":"Batmaz Z, Yurekli A, Bilge A, Kaleli C (2019) A review on deep learning for recommender systems: challenges and remedies. Artif Intell Rev 52(1):1\u201337","journal-title":"Artif Intell Rev"},{"issue":"4","key":"5494_CR5","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/s13218-018-0560-x","volume":"32","author":"H Bharadhwaj","year":"2018","unstructured":"Bharadhwaj H, Joshi S (2018) Explanations for temporal recommendations. K\u00fcnstl Intell 32(4):267\u2013272. https:\/\/doi.org\/10.1007\/s13218-018-0560-x","journal-title":"K\u00fcnstl Intell"},{"key":"5494_CR6","unstructured":"Bilgic M, Mooney RJ (2005) Explaining recommendations: satisfaction vs. promotion. In: Beyond personalization workshop, IUI, vol\u00a05. p 153"},{"issue":"1","key":"5494_CR7","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1162\/neco.1995.7.1.108","volume":"7","author":"CM Bishop","year":"1995","unstructured":"Bishop CM (1995) Training with noise is equivalent to Tikhonov regularization. Neural Comput 7(1):108\u2013116","journal-title":"Neural Comput"},{"issue":"7","key":"5494_CR8","doi-asserted-by":"publisher","first-page":"2441","DOI":"10.3390\/app10072441","volume":"10","author":"J Bobadilla","year":"2020","unstructured":"Bobadilla J, Alonso S, Hernando A (2020) Deep learning architecture for collaborative filtering recommender systems. Appl Sci 10(7):2441","journal-title":"Appl Sci"},{"issue":"1","key":"5494_CR9","doi-asserted-by":"publisher","first-page":"68","DOI":"10.9781\/ijimai.2020.02.006","volume":"6","author":"J Bobadilla","year":"2020","unstructured":"Bobadilla J, Ortega F, Guti\u00e9rrez A, Alonso S (2020) Classification-based deep neural network architecture for collaborative filtering recommender systems. IJIMAI 6(1):68\u201377. https:\/\/doi.org\/10.9781\/ijimai.2020.02.006","journal-title":"IJIMAI"},{"key":"5494_CR10","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","volume":"46","author":"J Bobadilla","year":"2013","unstructured":"Bobadilla J, Ortega F, Hernando A, Guti\u00e9rrez A (2013) Recommender systems survey. Knowl Based Syst 46:109\u2013132","journal-title":"Knowl Based Syst"},{"key":"5494_CR11","doi-asserted-by":"publisher","unstructured":"Bobadilla J, Serradilla F (2020) The effect of sparsity on collaborative filtering metrics. In: Proceedings of the twentieth australasian conference on australasian database - 92. pp 9\u201318. https:\/\/doi.org\/10.5555\/1862681.1862686","DOI":"10.5555\/1862681.1862686"},{"key":"5494_CR12","doi-asserted-by":"publisher","DOI":"10.9781\/ijimai.2020.11.001","author":"J Bobadilla","year":"2020","unstructured":"Bobadilla J, Lara-Cabrera R, Gonz\u00e1lez-Prieto \u00c1, Ortega F (2020) Deepfair: deep learning for improving fairness in recommender systems. Int J Interact Multimed Artif Intell. https:\/\/doi.org\/10.9781\/ijimai.2020.11.001","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"5494_CR13","unstructured":"Burke R, Sonboli N, Ordonez-Gauger A (2018) Balanced neighborhoods for multi-sided fairness in recommendation. In: Friedler SA, Wilson C (eds) Proceedings of the 1st conference on fairness, accountability and transparency, proceedings of machine learning research, vol\u00a081. PMLR, New York, NY, USA, pp 202\u2013214"},{"issue":"5","key":"5494_CR14","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1145\/3376898","volume":"63","author":"A Chouldechova","year":"2020","unstructured":"Chouldechova A, Roth A (2020) A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5):82\u201389. https:\/\/doi.org\/10.1145\/3376898","journal-title":"Commun. ACM"},{"issue":"1\u20132","key":"5494_CR15","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/S0004-3702(03)00079-1","volume":"151","author":"M Dash","year":"2003","unstructured":"Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1\u20132):155\u2013176","journal-title":"Artif Intell"},{"key":"5494_CR16","doi-asserted-by":"publisher","unstructured":"Ekstrand MD, Tian M, Kazi MRI, Mehrpouyan H, Kluver D (2020) Exploring author gender in book rating and recommendation. In: Proceedings of the 12th ACM conference on recommender systems. pp 242\u2013250. https:\/\/doi.org\/10.1145\/3240323.3240373","DOI":"10.1145\/3240323.3240373"},{"issue":"1","key":"5494_CR17","first-page":"438","volume":"12","author":"S Faizollahzadeh\u00a0Ardabili","year":"2018","unstructured":"Faizollahzadeh\u00a0Ardabili S, Najafi B, Shamshirband S, Minaei\u00a0Bidgoli B, Deo RC, Chau Kw (2018) Computational intelligence approach for modeling hydrogen production: a review. Eng Appl. Comput. Fluid Mech. 12(1):438\u2013458","journal-title":"Eng Appl. Comput. Fluid Mech."},{"key":"5494_CR18","doi-asserted-by":"publisher","first-page":"25111","DOI":"10.1109\/ACCESS.2020.2970836","volume":"8","author":"Y Fan","year":"2020","unstructured":"Fan Y, Xu K, Wu H, Zheng Y, Tao B (2020) Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition, MLP and LSTM network. IEEE Access 8:25111\u201325121","journal-title":"IEEE Access"},{"key":"5494_CR19","unstructured":"Gatys L, Ecker AS, Bethge M (2015) Texture synthesis using convolutional neural networks. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in neural information processing systems 28. Curran Associates, Inc, pp 262\u2013270"},{"key":"5494_CR20","doi-asserted-by":"publisher","unstructured":"Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, USA, June 27\u201330, 2016. IEEE Computer Society, pp 2414\u20132423. https:\/\/doi.org\/10.1109\/CVPR.2016.265","DOI":"10.1109\/CVPR.2016.265"},{"key":"5494_CR21","unstructured":"Hall MA (1999) Correlation-based feature selection for machine learning"},{"key":"5494_CR22","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.knosys.2015.12.018","volume":"97","author":"A Hernando","year":"2016","unstructured":"Hernando A, Bobadilla J, Ortega F (2016) A non negative matrix factorization for collaborative filtering recommender systems based on a bayesian probabilistic model. Knowl Based Syst 97:188\u2013202","journal-title":"Knowl Based Syst"},{"key":"5494_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2013.03.018","volume":"239","author":"A Hernando","year":"2013","unstructured":"Hernando A, Bobadilla J, Ortega F, Guti\u00e9rrez A (2013) Trees for explaining recommendations made through collaborative filtering. Inf Sci 239:1\u201317. https:\/\/doi.org\/10.1016\/j.ins.2013.03.018","journal-title":"Inf Sci"},{"key":"5494_CR24","doi-asserted-by":"publisher","unstructured":"Holstein K, Vaughan JW, III HD, Dud\u00edk M, Wallach HM (2019) Improving fairness in machine learning systems: What do industry practitioners need? In: Brewster SA, Fitzpatrick G, Cox AL, Kostakos V (eds) Proceedings of the 2019 CHI conference on human factors in computing systems, CHI 2019, Glasgow, Scotland, UK, May 04\u201309, 2019. ACM, p 600. https:\/\/doi.org\/10.1145\/3290605.3300830","DOI":"10.1145\/3290605.3300830"},{"key":"5494_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04920-9","author":"T Huang","year":"2020","unstructured":"Huang T, Zhang D, Bi L (2020) Neural embedding collaborative filtering for recommender systems. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-04920-9","journal-title":"Neural Comput Appl"},{"issue":"7","key":"5494_CR26","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1016\/j.patcog.2015.01.023","volume":"48","author":"F Jiang","year":"2015","unstructured":"Jiang F, Sui Y, Zhou L (2015) A relative decision entropy-based feature selection approach. Pattern Recognit 48(7):2151\u20132163. https:\/\/doi.org\/10.1016\/j.patcog.2015.01.023","journal-title":"Pattern Recognit"},{"issue":"12","key":"5494_CR27","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(12):8279\u20138287. https:\/\/doi.org\/10.1007\/s00521-018-3959-2","journal-title":"Neural Comput Appl"},{"key":"5494_CR28","unstructured":"Jing Y, Yang Y, Feng Z, Ye J, Yu Y, Song M (2019) Neural style transfer: a review. IEEE Trans Vis Comput Graph pp 1"},{"key":"5494_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-22440-4","volume-title":"Principal component analysis","author":"IT Jolliffe","year":"2002","unstructured":"Jolliffe IT (2002) Principal component analysis. Springer, New York. https:\/\/doi.org\/10.1007\/978-0-387-22440-4"},{"issue":"2322","key":"5494_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-59108-x","volume":"10","author":"A Krishnaswamy Rangarajan","year":"2020","unstructured":"Krishnaswamy Rangarajan A, Purushothaman R (2020) Disease classification in eggplant using pre-trained VGG16 and MSVM. Sci Rep 10(2322):1\u201311. https:\/\/doi.org\/10.1038\/s41598-020-59108-x","journal-title":"Sci Rep"},{"issue":"14","key":"5494_CR31","doi-asserted-by":"publisher","first-page":"4926","DOI":"10.3390\/app10144926","volume":"10","author":"R Lara-Cabrera","year":"2020","unstructured":"Lara-Cabrera R, Gonz\u00e1lez-Prieto \u00c1, Ortega F (2020) Deep matrix factorization approach for collaborative filtering recommender systems. Appl Sci 10(14):4926","journal-title":"Appl Sci"},{"key":"5494_CR32","unstructured":"Lee DD, Seung HS (2001) Algorithms for non-negative matrix factorization. In: Leen TK, Dietterich TG, Tresp V (eds) Advances in neural information processing systems 13. MIT Press, pp 556\u2013562"},{"key":"5494_CR33","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1145\/3184558.3186949","volume":"2018","author":"J Leonhardt","year":"2020","unstructured":"Leonhardt J, Anand A, Khosla M (2020) User fairness in recommender systems. Companion Proc Web Conf 2018:101\u2013102. https:\/\/doi.org\/10.1145\/3184558.3186949","journal-title":"Companion Proc Web Conf"},{"issue":"8","key":"5494_CR34","doi-asserted-by":"publisher","first-page":"2267","DOI":"10.1007\/s00521-015-2060-3","volume":"27","author":"F Li","year":"2016","unstructured":"Li F, Xu G, Cao L (2016) Two-level matrix factorization for recommender systems. Neural Comput Appl 27(8):2267\u20132278. https:\/\/doi.org\/10.1007\/s00521-015-2060-3","journal-title":"Neural Comput Appl"},{"key":"5494_CR35","unstructured":"Lin M, Chen Q, Yan S (2014) Network in network. CoRR arXiv:abs\/1312.4400"},{"key":"5494_CR36","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.procs.2018.09.020","volume":"137","author":"V Lully","year":"2018","unstructured":"Lully V, Laublet P, Stankovic M, Radulovic F (2018) Enhancing explanations in recommender systems with knowledge graphs. Procedia Comput Sci 137:211\u2013222. https:\/\/doi.org\/10.1016\/j.procs.2018.09.020","journal-title":"Procedia Comput Sci"},{"issue":"2","key":"5494_CR37","first-page":"109","volume":"8","author":"K Madadipouya","year":"2017","unstructured":"Madadipouya K, Chelliah S (2017) A literature review on recommender systems algorithms, techniques and evaluations. BRAIN Broad Res Artif Intell Neurosci 8(2):109\u2013124","journal-title":"BRAIN Broad Res Artif Intell Neurosci"},{"key":"5494_CR38","unstructured":"Mnih A, Salakhutdinov RR (2008) Probabilistic matrix factorization. In: Advances in neural information processing systems. pp. 1257\u20131264"},{"key":"5494_CR39","doi-asserted-by":"publisher","first-page":"69009","DOI":"10.1109\/ACCESS.2018.2880197","volume":"6","author":"R Mu","year":"2018","unstructured":"Mu R (2018) A survey of recommender systems based on deep learning. IEEE Access 6:69009\u201369022. https:\/\/doi.org\/10.1109\/ACCESS.2018.2880197","journal-title":"IEEE Access"},{"key":"5494_CR40","doi-asserted-by":"publisher","unstructured":"Ng AY (2020) Feature selection, L1 vs. L2 regularization, and rotational invariance. In: Proceedings of the twenty-first international conference on machine learning. p\u00a078. https:\/\/doi.org\/10.1145\/1015330.1015435","DOI":"10.1145\/1015330.1015435"},{"issue":"3","key":"5494_CR41","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s11257-017-9195-0","volume":"27","author":"I Nunes","year":"2017","unstructured":"Nunes I, Jannach D (2017) A systematic review and taxonomy of explanations in decision support and recommender systems. User Model User Adap Interface 27(3):393\u2013444. https:\/\/doi.org\/10.1007\/s11257-017-9195-0","journal-title":"User Model User Adap Interface"},{"key":"5494_CR42","doi-asserted-by":"crossref","unstructured":"Ortega F, Lara-Cabrera R, Gonz\u00e1lez-Prieto \u00c1, Bobadilla J (2020) Providing reliability in recommender systems through Bernoulli matrix factorization. arXiv preprint arXiv:2006.03481","DOI":"10.1016\/j.ins.2020.12.001"},{"issue":"3","key":"5494_CR43","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s10618-011-0215-0","volume":"24","author":"A Papadimitriou","year":"2012","unstructured":"Papadimitriou A, Symeonidis P, Manolopoulos Y (2012) A generalized taxonomy of explanations styles for traditional and social recommender systems. Data Min Knowl Discov 24(3):555\u2013583. https:\/\/doi.org\/10.1007\/s10618-011-0215-0","journal-title":"Data Min Knowl Discov"},{"issue":"8","key":"5494_CR44","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5494_CR45","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.eswa.2017.01.045","volume":"76","author":"L Quijano-Sanchez","year":"2017","unstructured":"Quijano-Sanchez L, Sauer C, Recio-Garcia JA, Diaz-Agudo B (2017) Make it personal: a social explanation system applied to group recommendations. Expert Syst Appl 76:36\u201348. https:\/\/doi.org\/10.1016\/j.eswa.2017.01.045","journal-title":"Expert Syst Appl"},{"key":"5494_CR46","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/4937.001.0001","volume-title":"Neural smithing: supervised learning in feedforward artificial neural networks","author":"R Reed","year":"1999","unstructured":"Reed R, MarksII RJ (1999) Neural smithing: supervised learning in feedforward artificial neural networks. Mit Press, New York"},{"key":"5494_CR47","first-page":"281","volume-title":"Social recommender systems","author":"A Rezvanian","year":"2019","unstructured":"Rezvanian A, Moradabadi B, Ghavipour M, Khomami MMD, Meybodi MR (2019) Social recommender systems. Springer, Berlin, pp 281\u2013313"},{"key":"5494_CR48","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parik, D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV). pp 618\u2013626 . https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"},{"key":"5494_CR49","doi-asserted-by":"publisher","first-page":"164650","DOI":"10.1109\/ACCESS.2019.2951750","volume":"7","author":"S Shamshirband","year":"2019","unstructured":"Shamshirband S, Rabczuk T, Chau KW (2019) A survey of deep learning techniques: application in wind and solar energy resources. IEEE Access 7:164650\u2013164666","journal-title":"IEEE Access"},{"issue":"4","key":"5494_CR50","doi-asserted-by":"publisher","first-page":"132","DOI":"10.25103\/jestr.104.18","volume":"10","author":"SS Sohail","year":"2017","unstructured":"Sohail SS, Siddiqui J, Ali R (2017) Classifications of recommender systems: a review. J Eng Sci Technol Rev 10(4):132\u2013153","journal-title":"J Eng Sci Technol Rev"},{"key":"5494_CR51","unstructured":"Tsintzou V, Pitoura E, Tsaparas P (2018) Bias disparity in recommendation systems. Preprint: arXiv"},{"key":"5494_CR52","doi-asserted-by":"publisher","first-page":"108581","DOI":"10.1109\/ACCESS.2019.2933048","volume":"7","author":"P Valdiviezo-Diaz","year":"2019","unstructured":"Valdiviezo-Diaz P, Ortega F, Cobos E, Lara-Cabrera R (2019) A collaborative filtering approach based on Na\u00efve Bayes classifier. IEEE Access 7:108581\u2013108592. https:\/\/doi.org\/10.1109\/ACCESS.2019.2933048","journal-title":"IEEE Access"},{"key":"5494_CR53","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.knosys.2017.11.003","volume":"140","author":"NM Villegas","year":"2018","unstructured":"Villegas NM, S\u00e1nchez C, D\u00edaz-Cely J, Tamura G (2018) Characterizing context-aware recommender systems: a systematic literature review. Knowl Based Syst 140:173\u2013200","journal-title":"Knowl Based Syst"},{"key":"5494_CR54","doi-asserted-by":"publisher","first-page":"113216","DOI":"10.1016\/j.eswa.2020.113216","volume":"150","author":"Wc Wang","year":"2020","unstructured":"Wang Wc, Xu L, Chau Kw, Xu Dm (2020) Yin-yang firefly algorithm based on dimensionally Cauchy mutation. Expert Syst Appl 150:113216","journal-title":"Expert Syst Appl"},{"key":"5494_CR55","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.cad.2018.04.003","volume":"101","author":"X Wang","year":"2018","unstructured":"Wang X, Qian X (2018) Total variance based feature point selection and applications. Comput Aided Des 101:37\u201356. https:\/\/doi.org\/10.1016\/j.cad.2018.04.003","journal-title":"Comput Aided Des"},{"key":"5494_CR56","doi-asserted-by":"crossref","unstructured":"Wen L, Li X, Li X, Gao L (2019) A new transfer learning based on vgg-19 network for fault diagnosis. In: 2019 IEEE 23rd international conference on computer supported cooperative work in design (CSCWD). pp 205\u2013209","DOI":"10.1109\/CSCWD.2019.8791884"},{"issue":"3","key":"5494_CR57","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1016\/j.engappai.2012.05.023","volume":"26","author":"C Wu","year":"2013","unstructured":"Wu C, Chau KW (2013) Prediction of rainfall time series using modular soft computingmethods. Eng Appl Artif Intell 26(3):997\u20131007","journal-title":"Eng Appl Artif Intell"},{"key":"5494_CR58","unstructured":"Yao S, Huang B (2017) Beyond parity: fairness objectives for collaborative filtering. In: Advances in neural information processing systems. pp 2921\u20132930"},{"issue":"5","key":"5494_CR59","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s10791-018-9327-0","volume":"21","author":"H Zamani","year":"2018","unstructured":"Zamani H, Shakery A (2018) A language model-based framework for multi-publisher content-based recommender systems. Inf Retr J 21(5):369\u2013409","journal-title":"Inf Retr J"},{"key":"5494_CR60","doi-asserted-by":"publisher","unstructured":"Zanker M, Ninaus D (2020) Knowledgeable explanations for recommender systems. In: IEEE date of conference: 31 Aug.\u20133 Sept 2010. https:\/\/doi.org\/10.1109\/WI-IAT.2010.131","DOI":"10.1109\/WI-IAT.2010.131"},{"key":"5494_CR61","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). pp 2921\u20132929","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05494-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05494-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05494-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,30]],"date-time":"2021-05-30T19:38:53Z","timestamp":1622403533000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05494-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,23]]},"references-count":61,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["5494"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05494-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,23]]},"assertion":[{"value":"5 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}