{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T17:22:19Z","timestamp":1771521739317,"version":"3.50.1"},"reference-count":90,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Deanship of Scientific Research","award":["NU\/RCP\/SERC\/12\/6"],"award-info":[{"award-number":["NU\/RCP\/SERC\/12\/6"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Computing"],"DOI":"10.1007\/s10791-025-09518-0","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T06:08:15Z","timestamp":1744351695000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysis of social data for accuracy improvement of collaborative filtering in MOOCs using text mining and deep learning techniques"],"prefix":"10.1007","volume":"28","author":[{"given":"Abdullah","family":"Alghamdi","sequence":"first","affiliation":[]},{"given":"Mehrbakhsh","family":"Nilashi","sequence":"additional","affiliation":[]},{"given":"Rabab Ali","family":"Abumalloh","sequence":"additional","affiliation":[]},{"given":"Hossein","family":"Ahmadi","sequence":"additional","affiliation":[]},{"given":"Mesfer","family":"Alrizq","sequence":"additional","affiliation":[]},{"given":"Sultan","family":"Alyami","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,11]]},"reference":[{"key":"9518_CR1","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.ins.2014.01.015","volume":"275","author":"CP Chen","year":"2014","unstructured":"Chen CP, Zhang C-Y. Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Inf Sci. 2014;275:314\u201347.","journal-title":"Inf Sci"},{"key":"9518_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2022.108541","volume":"251","author":"S Bag","year":"2022","unstructured":"Bag S, Rahman MS, Srivastava G, Chan H-L, Bryde DJ. The role of big data and predictive analytics in developing a resilient supply chain network in the South African mining industry against extreme weather events. Int J Prod Econ. 2022;251: 108541.","journal-title":"Int J Prod Econ"},{"key":"9518_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2021.107176","volume":"130","author":"A Honora","year":"2022","unstructured":"Honora A, Wang K-Y, Chih W-H. How does information overload about COVID-19 vaccines influence individuals\u2019 vaccination intentions? The roles of cyberchondria, perceived risk, and vaccine skepticism. Comput Hum Behav. 2022;130: 107176.","journal-title":"Comput Hum Behav"},{"key":"9518_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2021.103052","volume":"43","author":"M Kim","year":"2021","unstructured":"Kim M, Jeong S-G, Park J, Lee J-H. Assessment of pre-filter systems to control indoor inflow of particulate matter. J Build Eng. 2021;43: 103052.","journal-title":"J Build Eng"},{"key":"9518_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2022.121802","volume":"182","author":"H-Y Choi","year":"2022","unstructured":"Choi H-Y, Park J. Do data-driven CSR initiatives improve CSR performance? The importance of big data analytics capability. Technol Forecast Soc Chang. 2022;182: 121802.","journal-title":"Technol Forecast Soc Chang"},{"key":"9518_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2022.133661","volume":"372","author":"P Agyemang","year":"2022","unstructured":"Agyemang P, Kwofie EM, Fabrice A. Integrating framework analysis, scenario design, and decision support system for sustainable healthy food system analysis. J Clean Prod. 2022;372: 133661.","journal-title":"J Clean Prod"},{"key":"9518_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2022.112960","volume":"165","author":"G Ma","year":"2022","unstructured":"Ma G, Ma J, Li H, Wang Y, Wang Z, Zhang B. Customer behavior in purchasing energy-saving products: Big data analytics from online reviews of e-commerce. Energy Policy. 2022;165: 112960.","journal-title":"Energy Policy"},{"key":"9518_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jretconser.2022.102989","volume":"67","author":"M Alzate","year":"2022","unstructured":"Alzate M, Arce-Urriza M, Cebollada J. Mining the text of online consumer reviews to analyze brand image and brand positioning. J Retail Consum Serv. 2022;67: 102989.","journal-title":"J Retail Consum Serv"},{"key":"9518_CR9","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.dsm.2021.09.001","volume":"3","author":"N Zhang","year":"2021","unstructured":"Zhang N, Liu R, Zhang X-Y, Pang Z-L. The impact of consumer perceived value on repeat purchase intention based on online reviews: by the method of text mining. Data Sci Manag. 2021;3:22\u201332.","journal-title":"Data Sci Manag"},{"issue":"4","key":"9518_CR10","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1080\/0144929X.2015.1128977","volume":"35","author":"M-I Dascalu","year":"2016","unstructured":"Dascalu M-I, Bodea C-N, Mihailescu MN, Tanase EA, Ordo\u00f1ez de Pablos P. Educational recommender systems and their application in lifelong learning. Behav Inform Technol. 2016;35(4):290\u20137.","journal-title":"Behav Inform Technol"},{"key":"9518_CR11","doi-asserted-by":"crossref","unstructured":"Cui L-Z, Guo F-L, Liang Y-J. Research overview of educational recommender systems. In: Proceedings of the 2nd international conference on computer science and application engineering; 2018. pp. 1\u20137.","DOI":"10.1145\/3207677.3278071"},{"key":"9518_CR12","doi-asserted-by":"crossref","unstructured":"Jannach D, Pu P, Ricci F, Zanker M. Recommender systems: trends and frontiers. Wiley Online Library. 2022.","DOI":"10.1002\/aaai.12050"},{"key":"9518_CR13","doi-asserted-by":"crossref","unstructured":"Garcia-Martinez S, Hamou-Lhadj A. Educational recommender systems: a pedagogical-focused perspective. In: Multimedia services in intelligent environments. Springer; 2013. pp. 113\u201324.","DOI":"10.1007\/978-3-319-00375-7_8"},{"key":"9518_CR14","doi-asserted-by":"crossref","unstructured":"Barria-Pineda J. Exploring the need for transparency in educational recommender systems. In: Proceedings of the 28th ACM conference on user modeling, adaptation and personalization; 2020. pp. 376\u20139.","DOI":"10.1145\/3340631.3398676"},{"key":"9518_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115375","volume":"183","author":"IB Sassi","year":"2021","unstructured":"Sassi IB, Yahia SB, Liiv I. MORec: at the crossroads of context-aware and multi-criteria decision making for online music recommendation. Expert Syst Appl. 2021;183: 115375.","journal-title":"Expert Syst Appl"},{"key":"9518_CR16","doi-asserted-by":"crossref","unstructured":"Nilashi M, Bin Ibrahim O, Ithnin N. Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and neuro-fuzzy system. Knowl-Based Syst. 2014;60:82\u2013101.","DOI":"10.1016\/j.knosys.2014.01.006"},{"key":"9518_CR17","doi-asserted-by":"crossref","unstructured":"Jannach D, Karakaya Z, Gedikli F. Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 13th ACM conference on electronic commerce; 2012. pp. 674\u201389.","DOI":"10.1145\/2229012.2229065"},{"key":"9518_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.019","volume":"187","author":"N Nassar","year":"2020","unstructured":"Nassar N, Jafar A, Rahhal Y. A novel deep multi-criteria collaborative filtering model for recommendation system. Knowl-Based Syst. 2020;187: 104811.","journal-title":"Knowl-Based Syst"},{"key":"9518_CR19","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.ins.2021.02.005","volume":"562","author":"M Hong","year":"2021","unstructured":"Hong M. Decrease and conquer-based parallel tensor factorization for diversity and real-time of multi-criteria recommendation. Inf Sci. 2021;562:259\u201378.","journal-title":"Inf Sci"},{"key":"9518_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105385","volume":"194","author":"A Esteban","year":"2020","unstructured":"Esteban A, Zafra A, Romero C. Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowl-Based Syst. 2020;194: 105385.","journal-title":"Knowl-Based Syst"},{"key":"9518_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114868","volume":"176","author":"K Zhang","year":"2021","unstructured":"Zhang K, Liu X, Wang W, Li J. Multi-criteria recommender system based on social relationships and criteria preferences. Expert Syst Appl. 2021;176: 114868.","journal-title":"Expert Syst Appl"},{"issue":"1","key":"9518_CR22","first-page":"112","volume":"21","author":"NA Albatayneh","year":"2018","unstructured":"Albatayneh NA, Ghauth KI, Chua F-F. Utilizing learners\u2019 negative ratings in semantic content-based recommender system for e-learning forum. J Educ Technol Soc. 2018;21(1):112\u201325.","journal-title":"J Educ Technol Soc"},{"key":"9518_CR23","doi-asserted-by":"crossref","unstructured":"Amin S, Zeb MA. A hybrid recommender system for MOOC integrating collaborative and content-based filtering. In: Federated learning. CRC Press; 2025. pp. 78\u201393.","DOI":"10.1201\/9781003466581-5"},{"key":"9518_CR24","doi-asserted-by":"crossref","unstructured":"Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006;313(5786):504\u20137.","DOI":"10.1126\/science.1127647"},{"key":"9518_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.uclim.2021.101078","volume":"41","author":"J Tian","year":"2022","unstructured":"Tian J, Liu Y, Zheng W, Yin L. Smog prediction based on the deep belief-BP neural network model (DBN-BP). Urban Climate. 2022;41: 101078.","journal-title":"Urban Climate"},{"key":"9518_CR26","unstructured":"Blei DM, Ng AY, Jordan MI. Latent Dirichlet allocation. J Mach Learn Res. 2003;3:993\u20131022."},{"key":"9518_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105213","volume":"114","author":"S Huang","year":"2022","unstructured":"Huang S, Zhang J, Yang C, Gu Q, Li M, Wang W. The interval grey QFD method for new product development: Integrate with LDA topic model to analyze online reviews. Eng Appl Artif Intell. 2022;114: 105213.","journal-title":"Eng Appl Artif Intell"},{"key":"9518_CR28","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.neunet.2012.09.018","volume":"37","author":"T Kohonen","year":"2013","unstructured":"Kohonen T. Essentials of the self-organizing map. Neural Netw. 2013;37:52\u201365.","journal-title":"Neural Netw"},{"key":"9518_CR29","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2014.02.069","volume":"149","author":"A De","year":"2015","unstructured":"De A, Guo C. An adaptive vector quantization approach for image segmentation based on SOM network. Neurocomputing. 2015;149:48\u201358.","journal-title":"Neurocomputing"},{"key":"9518_CR30","doi-asserted-by":"publisher","first-page":"7145","DOI":"10.1016\/j.matpr.2021.06.311","volume":"47","author":"R Goyal","year":"2021","unstructured":"Goyal R, Goyal SJ. Recommender system: an analytical report on decision making for large scale online social networks. Mater Today Proc. 2021;47:7145\u20138.","journal-title":"Mater Today Proc"},{"key":"9518_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107397","volume":"95","author":"PK Jain","year":"2021","unstructured":"Jain PK, Yekun EA, Pamula R, Srivastava G. Consumer recommendation prediction in online reviews using Cuckoo optimized machine learning models. Comput Electr Eng. 2021;95: 107397.","journal-title":"Comput Electr Eng"},{"key":"9518_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dss.2017.06.007","volume":"102","author":"H Hong","year":"2017","unstructured":"Hong H, Xu D, Wang GA, Fan W. Understanding the determinants of online review helpfulness: a meta-analytic investigation. Decis Support Syst. 2017;102:1\u201311.","journal-title":"Decis Support Syst"},{"key":"9518_CR33","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.jbusres.2021.11.010","volume":"140","author":"Y Wang","year":"2022","unstructured":"Wang Y, Zhong K, Liu Q. Let criticism take precedence: effect of side order on consumer attitudes toward a two-sided online review. J Bus Res. 2022;140:403\u201319.","journal-title":"J Bus Res"},{"key":"9518_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.jretconser.2022.103066","volume":"68","author":"S Fernandes","year":"2022","unstructured":"Fernandes S, Panda R, Venkatesh V, Swar BN, Shi Y. Measuring the impact of online reviews on consumer purchase decisions\u2014a scale development study. J Retail Consum Serv. 2022;68: 103066.","journal-title":"J Retail Consum Serv"},{"key":"9518_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2022.107272","volume":"133","author":"Q Wang","year":"2022","unstructured":"Wang Q, Zhang W, Li J, Mai F, Ma Z. Effect of online review sentiment on product sales: the moderating role of review credibility perception. Comput Hum Behav. 2022;133: 107272.","journal-title":"Comput Hum Behav"},{"key":"9518_CR36","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.jbusres.2021.03.050","volume":"132","author":"C Liebrecht","year":"2021","unstructured":"Liebrecht C, Tsaousi C, van Hooijdonk C. Linguistic elements of conversational human voice in online brand communication: manipulations and perceptions. J Bus Res. 2021;132:124\u201335.","journal-title":"J Bus Res"},{"key":"9518_CR37","doi-asserted-by":"crossref","unstructured":"Yassine AF, Mohamed LA, Al Achhab M. Intelligent recommender system based on unsupervised machine learning and demographic attributes. Simul Model Pract Theory. 2021;107:102198.","DOI":"10.1016\/j.simpat.2020.102198"},{"issue":"1","key":"9518_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13174-018-0076-5","volume":"9","author":"CK Pereira","year":"2018","unstructured":"Pereira CK, Campos F, Str\u00f6ele V, David JMN, Braga R. BROAD-RSI\u2013educational recommender system using social networks interactions and linked data. J Internet Serv Appl. 2018;9(1):1\u201328.","journal-title":"J Internet Serv Appl"},{"key":"9518_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109361","volume":"127","author":"R Kuo","year":"2022","unstructured":"Kuo R, Cheng H-R. A content-based recommender system with consideration of repeat purchase behavior. Appl Soft Comput. 2022;127: 109361.","journal-title":"Appl Soft Comput"},{"key":"9518_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118058","volume":"208","author":"PK Biswas","year":"2022","unstructured":"Biswas PK, Liu S. A hybrid recommender system for recommending smartphones to prospective customers. Expert Syst Appl. 2022;208: 118058.","journal-title":"Expert Syst Appl"},{"key":"9518_CR41","doi-asserted-by":"crossref","unstructured":"Nam LN. Towards comprehensive approaches for the rating prediction phase in memory-based collaborative filtering recommender systems. 2022.","DOI":"10.1016\/j.ins.2021.12.123"},{"issue":"6","key":"9518_CR42","first-page":"907","volume":"17","author":"S Jiang","year":"2015","unstructured":"Jiang S, Qian X, Shen J, Fu Y, Mei T. Author topic model-based collaborative filtering for personalized POI recommendations. IEEE Trans Multimed. 2015;17(6):907\u201318.","journal-title":"IEEE Trans Multimed"},{"key":"9518_CR43","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.eswa.2017.12.020","volume":"97","author":"I Portugal","year":"2018","unstructured":"Portugal I, Alencar P, Cowan D. The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst Appl. 2018;97:205\u201327.","journal-title":"Expert Syst Appl"},{"key":"9518_CR44","doi-asserted-by":"crossref","unstructured":"Walek B, Fajmon P. A hybrid recommender system for an online store using a fuzzy expert system. Expert Syst Appl. 2023;212:118565.","DOI":"10.1016\/j.eswa.2022.118565"},{"key":"9518_CR45","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.ins.2019.06.005","volume":"501","author":"RG Mantovani","year":"2019","unstructured":"Mantovani RG, Rossi AL, Alcoba\u00e7a E, Vanschoren J, de Carvalho AC. A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers. Inf Sci. 2019;501:193\u2013221.","journal-title":"Inf Sci"},{"issue":"3","key":"9518_CR46","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/S0957-4174(02)00052-0","volume":"23","author":"YH Cho","year":"2002","unstructured":"Cho YH, Kim JK, Kim SH. A personalized recommender system based on web usage mining and decision tree induction. Expert Syst Appl. 2002;23(3):329\u201342.","journal-title":"Expert Syst Appl"},{"key":"9518_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117120","volume":"201","author":"R Ma","year":"2022","unstructured":"Ma R, Guo F, Li Z, Zhao L. Knowledge graph random neural networks for recommender systems. Expert Syst Appl. 2022;201: 117120.","journal-title":"Expert Syst Appl"},{"key":"9518_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117129","volume":"202","author":"M Kawai","year":"2022","unstructured":"Kawai M, Sato H, Shiohama T. Topic model-based recommender systems and their applications to cold-start problems. Expert Syst Appl. 2022;202: 117129.","journal-title":"Expert Syst Appl"},{"key":"9518_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117849","volume":"206","author":"A Pujahari","year":"2022","unstructured":"Pujahari A, Sisodia DS. Item feature refinement using matrix factorization and boosted learning based user profile generation for content-based recommender systems. Expert Syst Appl. 2022;206: 117849.","journal-title":"Expert Syst Appl"},{"key":"9518_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.108626","volume":"120","author":"H-H Wu","year":"2022","unstructured":"Wu H-H, Ke G, Wang Y, Chang Y-T. Prediction on recommender system based on bi-clustering and moth flame optimization. Appl Soft Comput. 2022;120: 108626.","journal-title":"Appl Soft Comput"},{"key":"9518_CR51","doi-asserted-by":"crossref","unstructured":"Ray B, Garain A, Sarkar R. An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Appl Soft Comput. 2021;98:106935.","DOI":"10.1016\/j.asoc.2020.106935"},{"key":"9518_CR52","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.jocs.2018.08.004","volume":"28","author":"E Inan","year":"2018","unstructured":"Inan E, Tekbacak F, Ozturk C. Moreopt: A goal programming based movie recommender system. J Comput Sci. 2018;28:43\u201350.","journal-title":"J Comput Sci"},{"issue":"1","key":"9518_CR53","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1007\/s10489-021-02363-w","volume":"52","author":"B Hui","year":"2022","unstructured":"Hui B, Zhang L, Zhou X, Wen X, Nian Y. Personalized recommendation system based on knowledge embedding and historical behavior. Appl Intell. 2022;52(1):954\u201366.","journal-title":"Appl Intell"},{"issue":"2","key":"9518_CR54","volume":"2","author":"R Shandilya","year":"2022","unstructured":"Shandilya R, Sharma S, Wong J. MATURE-food: food recommender system for MAndatory FeaTURE choices a system for enabling digital health. Int J Inform Manag Data Insights. 2022;2(2): 100090.","journal-title":"Int J Inform Manag Data Insights"},{"key":"9518_CR55","doi-asserted-by":"crossref","unstructured":"Hong M, Jung JJ. Multi-criteria tensor model for tourism recommender systems. Expert Syst Appl. 2021;170:114537.","DOI":"10.1016\/j.eswa.2020.114537"},{"key":"9518_CR56","doi-asserted-by":"crossref","unstructured":"Cremonesi P, Garzotto F, Turrin R. User-centric vs. system-centric evaluation of recommender systems. In: Human-computer interaction\u2014INTERACT 2013: 14th IFIP TC 13 International Conference, Cape Town, South Africa, September 2\u20136, 2013, Proceedings, Part III. Berlin: Springer; 2013. pp. 334\u201351.","DOI":"10.1007\/978-3-642-40477-1_21"},{"key":"9518_CR57","doi-asserted-by":"crossref","unstructured":"Boratto L, Fenu G, Marras M. The effect of algorithmic bias on recommender systems for massive open online courses. In: European conference on information retrieval. Cham: Springer; 2019. pp. 457\u201372.","DOI":"10.1007\/978-3-030-15712-8_30"},{"key":"9518_CR58","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.future.2021.08.025","volume":"127","author":"E G\u00f3mez","year":"2022","unstructured":"G\u00f3mez E, Zhang CS, Boratto L, Salam\u00f3 M, Ramos G. Enabling cross-continent provider fairness in educational recommender systems. Futur Gener Comput Syst. 2022;127:435\u201347.","journal-title":"Futur Gener Comput Syst"},{"issue":"3","key":"9518_CR59","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1007\/s40593-021-00271-1","volume":"32","author":"M Marras","year":"2022","unstructured":"Marras M, Boratto L, Ramos G, Fenu G. Equality of learning opportunity via individual fairness in personalized recommendations. Int J Artif Intell Educ. 2022;32(3):636\u201384.","journal-title":"Int J Artif Intell Educ"},{"issue":"1","key":"9518_CR60","doi-asserted-by":"publisher","first-page":"211","DOI":"10.2298\/CSIS090608021K","volume":"8","author":"A Kla\u0161nja-Mili\u0107evi\u0107","year":"2011","unstructured":"Kla\u0161nja-Mili\u0107evi\u0107 A, Vesin B, Ivanovi\u0107 M, Budimac Z. Integration of recommendations and adaptive hypermedia into Java tutoring system. Comput Sci Inf Syst. 2011;8(1):211\u201324.","journal-title":"Comput Sci Inf Syst"},{"key":"9518_CR61","unstructured":"Rezende P, Campos F, Braga R, David JM. Broad-rs: uma arquitetura para recomenda\u00e7\u00e3o de objetos de aprendizagem sens\u00edvel ao contexto usando agentes. In: Anais do X Congresso Brasileiro de Ensino Superior a Dist\u00e2ncia; 2013. pp. 11\u20133."},{"key":"9518_CR62","doi-asserted-by":"crossref","unstructured":"Gasparini I, Kemczinski A, Pimenta MS, de Oliveira JP. Modelo do usu\u00e1rio sens\u00edvel ao contexto cultural em um sistema e-learning adaptativo. Inform Educ Teoria Pr\u00e1t. 2011;14(1).","DOI":"10.22456\/1982-1654.21974"},{"issue":"4","key":"9518_CR63","first-page":"128","volume":"8","author":"P Karampiperis","year":"2005","unstructured":"Karampiperis P, Sampson D. Adaptive learning resources sequencing in educational hypermedia systems. J Educ Technol Soc. 2005;8(4):128\u201347.","journal-title":"J Educ Technol Soc"},{"issue":"3","key":"9518_CR64","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1076\/ilee.9.3.255.3568","volume":"9","author":"MM Recker","year":"2001","unstructured":"Recker MM, Wiley DA. A non-authoritative educational metadata ontology for filtering and recommending learning objects. Interact Learn Environ. 2001;9(3):255\u201371.","journal-title":"Interact Learn Environ"},{"key":"9518_CR65","doi-asserted-by":"crossref","unstructured":"Mazhoud O, Kalboussi A, Kacem AH. Educational recommender system based on learner's annotative activity. Int J Emerg Technol Learn. 2021;16(10).","DOI":"10.3991\/ijet.v16i10.19955"},{"issue":"3","key":"9518_CR66","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3390\/a4030131","volume":"4","author":"OC Santos","year":"2011","unstructured":"Santos OC, Boticario JG. Requirements for semantic educational recommender systems in formal e-learning scenarios. Algorithms. 2011;4(3):131\u201354.","journal-title":"Algorithms"},{"key":"9518_CR67","doi-asserted-by":"crossref","unstructured":"Barria-Pineda J, Akhuseyinoglu K, \u017delem-\u0106elap S, Brusilovsky P, Milicevic AK, Ivanovic M. Explainable recommendations in a personalized programming practice system. In: International conference on artificial intelligence in education. Cham: Springer; 2021. pp. 64\u201376.","DOI":"10.1007\/978-3-030-78292-4_6"},{"key":"9518_CR68","doi-asserted-by":"crossref","unstructured":"Ahmadian Yazdi H, Seyyed Mahdavi Chabok SJ, Kheirabadi M. Dynamic educational recommender system based on improved recurrent neural networks using attention technique. Appl Artif Intell. 2022;36(1):2005298.","DOI":"10.1080\/08839514.2021.2005298"},{"issue":"3","key":"9518_CR69","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1109\/MIS.2007.58","volume":"22","author":"G Adomavicius","year":"2007","unstructured":"Adomavicius G, Kwon Y. New recommendation techniques for multicriteria rating systems. IEEE Intell Syst. 2007;22(3):48\u201355.","journal-title":"IEEE Intell Syst"},{"key":"9518_CR70","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.asoc.2017.01.015","volume":"54","author":"X Qiu","year":"2017","unstructured":"Qiu X, Ren Y, Suganthan PN, Amaratunga GA. Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl Soft Comput. 2017;54:246\u201355.","journal-title":"Appl Soft Comput"},{"key":"9518_CR71","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.knosys.2017.05.022","volume":"130","author":"X Sun","year":"2017","unstructured":"Sun X, Li T, Li Q, Huang Y, Li Y. Deep belief echo-state network and its application to time series prediction. Knowl-Based Syst. 2017;130:17\u201329.","journal-title":"Knowl-Based Syst"},{"key":"9518_CR72","doi-asserted-by":"crossref","unstructured":"Dewi C, Chen RC, Hendry, Hung HT. Experiment improvement of restricted Boltzmann machine methods for image classification. Vietnam J Comput Sci. 2021;8(03):417\u201332.","DOI":"10.1142\/S2196888821500184"},{"issue":"1","key":"9518_CR73","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s10916-009-9340-3","volume":"35","author":"CC Tan","year":"2011","unstructured":"Tan CC, Eswaran C. Using autoencoders for mammogram compression. J Med Syst. 2011;35(1):49\u201358.","journal-title":"J Med Syst"},{"key":"9518_CR74","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","volume":"2","author":"X-W Chen","year":"2014","unstructured":"Chen X-W, Lin X. Big data deep learning: challenges and perspectives. IEEE Access. 2014;2:514\u201325.","journal-title":"IEEE Access"},{"issue":"2","key":"9518_CR75","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1007\/s10791-010-9141-9","volume":"14","author":"Y Lu","year":"2011","unstructured":"Lu Y, Mei Q, Zhai C. Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA. Inf Retrieval. 2011;14(2):178\u2013203.","journal-title":"Inf Retrieval"},{"key":"9518_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.tele.2021.101597","volume":"61","author":"M Nilashi","year":"2021","unstructured":"Nilashi M, et al. Recommendation agents and information sharing through social media for coronavirus outbreak. Telem Inform. 2021;61: 101597.","journal-title":"Telem Inform"},{"issue":"1","key":"9518_CR77","doi-asserted-by":"publisher","first-page":"48","DOI":"10.3141\/2552-07","volume":"2552","author":"S Das","year":"2016","unstructured":"Das S, Sun X, Dutta A. Text mining and topic modeling of compendiums of papers from transportation research board annual meetings. Transp Res Rec. 2016;2552(1):48\u201356.","journal-title":"Transp Res Rec"},{"key":"9518_CR78","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.measurement.2019.01.013","volume":"145","author":"F Nan","year":"2019","unstructured":"Nan F, Li Y, Jia X, Dong L, Chen Y. Application of improved som network in gene data cluster analysis. Measurement. 2019;145:370\u20138.","journal-title":"Measurement"},{"key":"9518_CR79","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.ins.2020.05.102","volume":"537","author":"L Sun","year":"2020","unstructured":"Sun L, Yin T, Ding W, Qian Y, Xu J. Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems. Inf Sci. 2020;537:401\u201324.","journal-title":"Inf Sci"},{"issue":"11","key":"9518_CR80","doi-asserted-by":"publisher","first-page":"3127","DOI":"10.3390\/s20113127","volume":"20","author":"MH Chowdhury","year":"2020","unstructured":"Chowdhury MH, et al. Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques. Sensors. 2020;20(11):3127.","journal-title":"Sensors"},{"key":"9518_CR81","doi-asserted-by":"crossref","unstructured":"El Kharki A, Mechbouh J, Wahbi M, Alaoui OY, Boulaassal H, Maatouk M, El Kharki O. Optimizing SVM for argan tree classification using Sentinel-2 data: a case study in the Sous-Massa Region, Morocco. Rev Teledet. 2025(65).","DOI":"10.4995\/raet.2025.22060"},{"key":"9518_CR82","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemosphere.2025.144074","volume":"372","author":"S Kanji","year":"2025","unstructured":"Kanji S, Das S, Rajak C. A comparative hydrochemical assessment of groundwater quality for drinking and irrigation purposes using different statistical and ML models in lower gangetic alluvial plain, eastern India. Chemosphere. 2025;372: 144074.","journal-title":"Chemosphere"},{"key":"9518_CR83","unstructured":"Girod B. What's wrong with mean-squared error?. In: Digital images and human vision; 1993. pp. 207\u201320."},{"key":"9518_CR84","first-page":"202","volume":"2","author":"M Demircan","year":"2021","unstructured":"Demircan M, Seller A, Abut F, Akay MF. Developing Turkish sentiment analysis models using machine learning and e-commerce data. Int J Cogn Comput Eng. 2021;2:202\u20137.","journal-title":"Int J Cogn Comput Eng"},{"key":"9518_CR85","doi-asserted-by":"crossref","unstructured":"Nilashi M, bin Ibrahim O, Ithnin N, Sarmin NH. A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA\u2013ANFIS. Electron Commerce Res Appl. 2015;14(6):542\u201362.","DOI":"10.1016\/j.elerap.2015.08.004"},{"key":"9518_CR86","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.knosys.2013.12.007","volume":"57","author":"G Guo","year":"2014","unstructured":"Guo G, Zhang J, Thalmann D. Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowl-Based Syst. 2014;57:57\u201368.","journal-title":"Knowl-Based Syst"},{"key":"9518_CR87","doi-asserted-by":"crossref","unstructured":"Qin J, Zeng M. An integrated method for product ranking through online reviews based on evidential reasoning theory and stochastic dominance. Inform Sci. 2022.","DOI":"10.1016\/j.ins.2022.08.070"},{"issue":"12","key":"9518_CR88","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1109\/TNN.2011.2171713","volume":"22","author":"H He","year":"2011","unstructured":"He H, Chen S, Li K, Xu X. Incremental learning from stream data. IEEE Trans Neural Networks. 2011;22(12):1901\u201314.","journal-title":"IEEE Trans Neural Networks"},{"key":"9518_CR89","doi-asserted-by":"crossref","unstructured":"Hasib KM, Habib MA, Towhid NA, Showrov MIH. A novel deep learning based sentiment analysis of twitter data for us airline service. In: 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD). IEEE; 2021. pp. 450\u20135.","DOI":"10.1109\/ICICT4SD50815.2021.9396879"},{"key":"9518_CR90","doi-asserted-by":"crossref","unstructured":"Hasib KM, et al. A survey of methods for managing the classification and solution of data imbalance problem. arXiv preprint arXiv:2012.11870. 2020.","DOI":"10.3844\/jcssp.2020.1546.1557"}],"container-title":["Discover Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09518-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10791-025-09518-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10791-025-09518-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T12:02:25Z","timestamp":1746100945000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10791-025-09518-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,11]]},"references-count":90,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["9518"],"URL":"https:\/\/doi.org\/10.1007\/s10791-025-09518-0","relation":{},"ISSN":["2948-2992"],"issn-type":[{"value":"2948-2992","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,11]]},"assertion":[{"value":"10 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"33"}}