{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T18:09:08Z","timestamp":1785434948322,"version":"3.56.0"},"reference-count":92,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T00:00:00Z","timestamp":1780704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005378","name":"Hacettepe University","doi-asserted-by":"publisher","award":["SAY-2023-20686"],"award-info":[{"award-number":["SAY-2023-20686"]}],"id":[{"id":"10.13039\/501100005378","id-type":"DOI","asserted-by":"publisher"}]},{"award":["SAY-2023-20686"],"award-info":[{"award-number":["SAY-2023-20686"]}],"id":[{"id":"https:\/\/ror.org\/04kwvgz42","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JEMR"],"abstract":"<jats:p>This study aims to provide insight into consumer purchasing decisions by integrating eye-tracking data with forecasting techniques. First, the study investigates how consumption motives (hedonic vs. utilitarian) and purchasing purposes (for oneself vs. for others) influence visual attention and decision-making processes. An experimental design was conducted with 128 participants in a simulated online shopping environment, where eye-tracking data were collected based on fixation counts and durations across defined Areas of Interest (AOIs). Second, a total of 20 input features were collected, comprising fixation counts and fixation durations for 10 review-related Areas of Interest (AOIs), and these features were evaluated across the experimental scenarios, while the binary output variable represented the participant\u2019s purchase decision. These biometric features, together with scenario information, were used to forecast purchasing decisions using six machine-learning methods, including Artificial Neural Networks, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, and Logistic Regression. The results indicate that consumers\u2019 visual attention aligns with their consumption motives and purchasing purposes, revealing distinct gaze patterns across different scenarios. In the forecasting phase, the accuracy of different methods for predicting purchasing decisions using review-related eye-tracking data is evaluated. Support Vector Machines achieved the highest overall accuracy, approximately 59\u201360% across the evaluated datasets, compared with a validation-specific majority-class baseline of 53.85%. This corresponds to a modest improvement of approximately 5.15\u20136.15 percentage points over the naive benchmark. Overall, the findings suggest that objectively recorded review-related eye-tracking data can be operationalized as behavioral input features in a machine-learning-based purchase-decision classification framework, highlighting the methodological value of integrating eye-tracking insights with consumer behavior forecasting.<\/jats:p>","DOI":"10.3390\/jemr19030064","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T07:43:02Z","timestamp":1780904582000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Eye-Tracking and Forecasting Experiment on Consumer Purchasing Decisions Through Product Reviews"],"prefix":"10.3390","volume":"19","author":[{"given":"Seda Busra","family":"Sarac","sequence":"first","affiliation":[{"name":"Department of Business Administration, Hacettepe University, 06800 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0786-9641","authenticated-orcid":false,"given":"Kazim Baris","family":"Atici","sequence":"additional","affiliation":[{"name":"Department of Business Administration, Hacettepe University, 06800 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6259-0577","authenticated-orcid":false,"given":"Ismail","family":"Bezci","sequence":"additional","affiliation":[{"name":"Department of Interior Architecture and Environmental Design, Faculty of Fine Arts, Hacettepe University, 06800 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4832-0415","authenticated-orcid":false,"given":"Ata Erinc","family":"Dansuk","sequence":"additional","affiliation":[{"name":"Presify Analytics, METU Informatics Innovation Center, 06510 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatma Semira","family":"Yildirim","sequence":"additional","affiliation":[{"name":"Department of Business Administration, Ba\u015fkent University, 06790 Ankara, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,6]]},"reference":[{"key":"ref_1","unstructured":"Ratneshwar, S., Mick, D.G., and Huffman, C. (2000). Introduction: The \u201cwhy\u201d of consumption. The Why of Consumption: Contemporary Perspectives on Consumer Motives, Goals, and Desires, Routledge."},{"key":"ref_2","unstructured":"Schiffman, L., O\u2019Cass, A., Paladino, A., and Carlson, J. (2013). Consumer Behaviour, Pearson Higher Education AU."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1111\/j.1559-1816.1998.tb01668.x","article-title":"Locus of control for consumer outcomes: Predicting consumer behavior","volume":"28","author":"Busseri","year":"1998","journal-title":"J. Appl. Soc. Psychol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"39","DOI":"10.2501\/JAR-2015-028","article-title":"How to capture consumer experiences: A context-specific approach to measuring engagement across qualitatively different experiences","volume":"56","author":"Calder","year":"2016","journal-title":"J. Advert. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MPUL.2012.2189167","article-title":"Predicting consumer behavior: Using novel mind-reading approaches","volume":"3","author":"Calvert","year":"2012","journal-title":"IEEE Pulse"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"17486","DOI":"10.1073\/pnas.1005962107","article-title":"Predicting consumer behavior with web search","volume":"107","author":"Goel","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kooti, F., Lerman, K., Aiello, L.M., Grbovic, M., Djuric, N., and Radosavljevic, V. (2016). Portrait of an online shopper: Understanding and predicting consumer behavior. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining (WSDM \u201916), San Francisco, CA USA, 22\u201325 February 2016, Association for Computing Machinery.","DOI":"10.1145\/2835776.2835831"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s40309-017-0125-5","article-title":"The future of consumer decision making","volume":"5","year":"2017","journal-title":"Eur. J. Futures Res."},{"key":"ref_9","unstructured":"Solomon, M.R. (2020). Consumer Behavior: Buying, Having, and Being, Pearson. [13th ed.]."},{"key":"ref_10","unstructured":"Wilhelms, R. (2008). Motivational Factors in Consumer Decisions to Participate in Soccer Lotteries: An International Marketing Study. [Doctoral Dissertation, Alliant International University, Marshall Goldsmith School of Management]."},{"key":"ref_11","unstructured":"Bower, G.H. (1991). Deriving categories to achieve goals. Psychology of Learning and Motivation, Academic Press."},{"key":"ref_12","unstructured":"Koyuncu, O. (2022). Utilitarian and hedonic shopping motives. Business Research: In Memory of Prof. Dr. Hasan I\u015f\u0131n Dener, Hacettepe University Publishing."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.jretai.2019.10.003","article-title":"The role of hedonic and utilitarian motives on the effectiveness of partitioned pricing","volume":"96","author":"Choi","year":"2020","journal-title":"J. Retail."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1509\/jmkr.47.6.1090","article-title":"Price-framing effects on the purchase of hedonic and utilitarian bundles","volume":"47","author":"Khan","year":"2010","journal-title":"J. Mark. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1177\/002224298204600314","article-title":"Hedonic consumption: Emerging concepts, methods, and propositions","volume":"46","author":"Hirschman","year":"1982","journal-title":"J. Mark."},{"key":"ref_16","unstructured":"Kotler, P. (2001). Marketing Management: Millennium Edition, Prentice Hall."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40708-022-00175-3","article-title":"A systematic review of the prediction of consumer preference using EEG measures and machine learning in neuromarketing research","volume":"9","author":"Byrne","year":"2022","journal-title":"Brain Inform."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1002\/mar.20206","article-title":"Validity, reliability, and applicability of psychophysiological techniques in marketing research","volume":"25","author":"Wang","year":"2008","journal-title":"Psychol. Mark."},{"key":"ref_19","unstructured":"Lansing, J.B. (1955). Consumer anticipations: Their use in forecasting consumer behavior. Short-Term Economic Forecasting, Princeton University Press."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ijpsycho.2006.03.007","article-title":"What is \u2018neuromarketing\u2019? A discussion and agenda for future research","volume":"63","author":"Lee","year":"2007","journal-title":"Int. J. Psychophysiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/nrn2795","article-title":"Neuromarketing: The hope and hype of neuroimaging in business","volume":"11","author":"Berns","year":"2010","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.jretai.2024.02.005","article-title":"Utilising eye-tracking data in retailing field research: A practical guide","volume":"100","author":"Ahlbom","year":"2024","journal-title":"J. Retail."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1093\/jcr\/ucad078","article-title":"Curvy digital marketing designs: Virtual elements with rounded shapes enhance online click-through rates","volume":"51","author":"Biswas","year":"2024","journal-title":"J. Consum. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1093\/jcr\/ucab017","article-title":"Online advertising suppresses visual competition during planned purchases","volume":"48","author":"Pieters","year":"2021","journal-title":"J. Consum. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1177\/07439156211061289","article-title":"When detailed information works better: Comparison of three- and five-color\/letter front-of-package nutrition labels","volume":"41","author":"Werle","year":"2022","journal-title":"J. Public Policy Mark."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Skourou, E., and Spiliotopoulos, D. (2024). Assessment of Purchasing Influence of Email Campaigns Using Eye Tracking. Multimodal Technol. Interact., 8.","DOI":"10.3390\/mti8100087"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1016\/0169-2070(88)90111-2","article-title":"Research needs in forecasting","volume":"4","author":"Armstrong","year":"1988","journal-title":"Int. J. Forecast."},{"key":"ref_28","unstructured":"Mothersbaugh, D.L., Hawkins, D.I., and Kleiser, S.B. (2019). Consumer Behavior: Building Marketing Strategy, McGraw-Hill Higher Education. [14th ed.]."},{"key":"ref_29","first-page":"76","article-title":"Factors affecting consumer buying behavior","volume":"2","author":"Ramya","year":"2016","journal-title":"Int. J. Appl. Res."},{"key":"ref_30","unstructured":"Armstrong, G., and Kotler, P. (2022). Marketing: An introduction, Pearson Education. [15th ed.]."},{"key":"ref_31","unstructured":"Fernie, J., and Arnold, S. (2002). A motivation-based shopper typology. Retailing: Critical Concepts. The Environments for Retailing, Routledge."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/BF00436035","article-title":"Measuring the hedonic and utilitarian sources of consumer attitudes","volume":"2","author":"Batra","year":"1991","journal-title":"Mark. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1207\/s15327663jcp1401&2_16","article-title":"Goal\u2013attribute compatibility in consumer choice","volume":"14","author":"Chernev","year":"2004","journal-title":"J. Consum. Psychol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1086\/209519","article-title":"Donations to charity as purchase incentives: How well they work may depend on what you are trying to sell","volume":"24","author":"Strahilevitz","year":"1998","journal-title":"J. Consum. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1108\/IJPDLM-05-2013-0150","article-title":"On the relationship between purchasing integration and purchasing decision-making speed","volume":"45","author":"Kaufmann","year":"2015","journal-title":"Int. J. Phys. Distrib. Logist. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1362\/146934708X290403","article-title":"Consumer involvement: A new perspective","volume":"8","author":"Michaelidou","year":"2008","journal-title":"Mark. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/0167-4870(89)90030-5","article-title":"A causal model of consumer involvement","volume":"10","author":"Mittal","year":"1989","journal-title":"J. Econ. Psychol."},{"key":"ref_38","unstructured":"Pan, J.-S., Chen, S.-M., and Nguyen, N.T. (2012). A comparison of buying decision patterns by product involvement: An eye-tracking approach. Intelligent Information and Database Systems: ACIIDS 2012. Lecture Notes in Computer Science, Springer."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1111\/ijcs.12899","article-title":"Online consumer shopping behaviour: A review and research agenda","volume":"47","author":"Singh","year":"2023","journal-title":"Int. J. Consum. Stud."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1080\/02650487.2019.1617651","article-title":"How consumers attend to online reviews: An eye-tracking and network analysis approach","volume":"39","author":"Maslowska","year":"2020","journal-title":"Int. J. Advert."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103563","DOI":"10.1016\/j.jretconser.2023.103563","article-title":"The effect of consumption goals on review helpfulness: Behavioral and eye-tracking research","volume":"76","author":"Chen","year":"2024","journal-title":"J. Retail. Consum. Serv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"185","DOI":"10.2307\/20721420","article-title":"Research note: What makes a helpful online review? A study of customer reviews on Amazon.com","volume":"34","author":"Mudambi","year":"2010","journal-title":"MIS Q."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.chb.2016.08.037","article-title":"Search product and experience product online reviews: An eye-tracking study on consumers\u2019 review search behavior","volume":"65","author":"Luan","year":"2016","journal-title":"Comput. Hum. Behav."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e13083","DOI":"10.1111\/ijcs.13083","article-title":"Product review reading and rating statistics: How do they affect consumer product evaluation?","volume":"48","author":"Guan","year":"2024","journal-title":"Int. J. Consum. Stud."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s10660-021-09491-y","article-title":"A study of cognitive effort involved in the framing effect of summary descriptions of online product reviews for search vs. experience products","volume":"23","author":"Jin","year":"2023","journal-title":"Electron. Commer. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s11002-005-5899-8","article-title":"Decision neuroscience","volume":"16","author":"Shiv","year":"2005","journal-title":"Mark. Lett."},{"key":"ref_47","unstructured":"\u021aichindelean, M., Cetin\u0103, I., and \u021aichindelean, M. (2018). Neuromarketing services: An analysis of international specialists\u2019 experience. Proceedings of the 12th International Management Conference: Management Perspectives in the Digital Era, Bucharest, Romania, 1\u20132 November 2018, The Bucharest University of Economic Studies."},{"key":"ref_48","first-page":"216","article-title":"Utilization of eye-tracking research approach in marketing: Current issues and future agendas","volume":"5","year":"2017","journal-title":"Mark. Identity"},{"key":"ref_49","first-page":"141","article-title":"Predicting preference from fixations","volume":"7","author":"Glaholt","year":"2009","journal-title":"PsychNology J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1057\/palgrave.bm.2550074","article-title":"Like more, look more; look more, like more: The evidence from eye-tracking","volume":"14","author":"Maughan","year":"2007","journal-title":"J. Brand Manag."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.3758\/s13428-017-0998-z","article-title":"Threats to the validity of eye-movement research in psychology","volume":"50","author":"Orquin","year":"2018","journal-title":"Behav. Res. Methods"},{"key":"ref_52","unstructured":"Holmqvist, K., Nystr\u00f6m, M., Andersson, R., Dewhurst, R., Jarodzka, H., and Van de Weijer, J. (2011). Eye Tracking: A Comprehensive Guide to Methods and Measures, Oxford University Press."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, T., Samaranayake, P., Cen, X., Qi, M., and Lan, Y.C. (2022). The impact of online reviews on consumers\u2019 purchasing decisions: Evidence from an eye-tracking study. Front. Psychol., 13.","DOI":"10.3389\/fpsyg.2022.865702"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1561\/1700000011","article-title":"Eye tracking for visual marketing","volume":"1","author":"Wedel","year":"2008","journal-title":"Found. Trends Mark."},{"key":"ref_55","unstructured":"Cambridge Forecasting (2026, January 16). In Cambridge Dictionary. Available online: https:\/\/dictionary.cambridge.org\/dictionary\/english\/forecasting."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1287\/msom.2015.0561","article-title":"Analytics for an online retailer: Demand forecasting and price optimization","volume":"18","author":"Ferreira","year":"2016","journal-title":"Manuf. Serv. Oper. Manag."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1420","DOI":"10.1016\/j.ijforecast.2020.02.005","article-title":"Daily retail demand forecasting using machine learning with emphasis on calendric special days","volume":"36","author":"Huber","year":"2020","journal-title":"Int. J. Forecast."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/j.indmarman.2019.05.003","article-title":"A big data driven framework for demand-driven forecasting with effects of marketing-mix variables","volume":"90","author":"Kumar","year":"2020","journal-title":"Ind. Mark. Manag."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1205\/cherd.04246","article-title":"Customer demand forecasting via support vector regression analysis","volume":"83","author":"Levis","year":"2005","journal-title":"Chem. Eng. Res. Des."},{"key":"ref_60","unstructured":"Egeli, B., Ozturan, M., and Badur, B. (2003, January 26\u201327). Stock market prediction using artificial neural networks. Proceedings of the 3rd International Conference on Business, Eindhoven, The Netherlands."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1016\/j.ijepes.2010.12.009","article-title":"Electricity price forecasting using artificial neural networks","volume":"33","author":"Singhal","year":"2011","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2681","DOI":"10.1109\/TVCG.2021.3067779","article-title":"FixationNet: Forecasting eye fixations in task-oriented virtual environments","volume":"27","author":"Hu","year":"2021","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.1007\/s12652-020-01839-w","article-title":"Stock market prediction using machine learning classifiers and social media news","volume":"13","author":"Khan","year":"2022","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Peshkovskaya, A.G., Babkina, T.S., Myagkov, M.G., Kulikov, I.A., Ekshova, K.V., and Harriff, K. (2017). The socialization effect on decision making in the Prisoner\u2019s Dilemma game: An eye-tracking study. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0175492"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1007\/s40881-019-00071-3","article-title":"When the eyes say buy: Visual fixations during hypothetical consumer choice improve prediction of actual purchases","volume":"5","author":"Imai","year":"2019","journal-title":"J. Econ. Sci. Assoc."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"114039","DOI":"10.1016\/j.dss.2023.114039","article-title":"All eyes on me: Predicting consumer intentions on social commerce platforms using eye-tracking data and ensemble learning","volume":"175","author":"Mikalef","year":"2023","journal-title":"Decis. Support Syst."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"\u0160ola, H.M., Qureshi, F.H., and Khawaja, S. (2024). AI-powered eye tracking for bias detection in online course reviews: A Udemy case study. Big Data Cogn. Comput., 8.","DOI":"10.3390\/bdcc8110144"},{"key":"ref_68","first-page":"1","article-title":"Data analytics in supply chain management: A review of machine learning applications in demand forecasting","volume":"14","author":"Aamer","year":"2021","journal-title":"Oper. Supply Chain Manag. Int. J."},{"key":"ref_69","first-page":"270","article-title":"Demand forecasting new fashion products: A review paper","volume":"44","author":"Anitha","year":"2024","journal-title":"J. Forecast."},{"key":"ref_70","first-page":"2104","article-title":"An analysis of the performance of artificial neural network technique for stock market forecasting","volume":"2","author":"Vaisla","year":"2010","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sydenham, P.H., and Thorn, R. (2005). Artificial neural networks. Handbook of Measuring System Design, Wiley.","DOI":"10.1002\/0471497398"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Parmar, A., Katariya, R., and Patel, V. (2019). A review on random forest: An ensemble classifier. Intelligent Data Communication Technologies and Internet of Things (ICICI 2018), Coimbatore, India, 7\u20138 August 2018, Springer.","DOI":"10.1007\/978-3-030-03146-6_86"},{"key":"ref_74","unstructured":"Ho, T.K. (1995). Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 24\u201327 August 1995, IEEE."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Rozenberg, G., B\u00e4ck, T., and Kok, J.N. (2012). SVM tutorial\u2014Classification, regression, and ranking. Handbook of Natural Computing, Springer.","DOI":"10.1007\/978-3-540-92910-9"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.4249\/scholarpedia.1883","article-title":"K-nearest neighbor","volume":"4","author":"Peterson","year":"2009","journal-title":"Scholarpedia"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Guo, G., Wang, H., Bell, D., Bi, Y., and Greer, K. (2003). KNN model-based approach in classification. On the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. Lecture Notes in Computer Science, Catania, Italy, 3\u20137 November 2003, Springer.","DOI":"10.1007\/978-3-540-39964-3_62"},{"key":"ref_78","unstructured":"IBM (2026, March 16). What are Na\u00efve Bayes Classifiers?. Available online: https:\/\/www.ibm.com\/topics\/naive-bayes."},{"key":"ref_79","unstructured":"Rish, I. (2001). An empirical study of the naive Bayes classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA, 4\u20136 August 2001, IBM Research."},{"key":"ref_80","unstructured":"Jurafsky, D., and Martin, J.H. (2026, May 30). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models, Available online: https:\/\/web.stanford.edu\/~jurafsky\/slp3."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/RBME.2021.3066072","article-title":"Review of eye tracking metrics involved in emotional and cognitive processes","volume":"16","author":"Skaramagkas","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.jeconom.2022.04.007","article-title":"Model averaging prediction by K-fold cross-validation","volume":"235","author":"Zhang","year":"2023","journal-title":"J. Econ."},{"key":"ref_83","first-page":"20220224","article-title":"Validation of machine learning ridge regression models using Monte Carlo, bootstrap, and variations in cross-validation","volume":"32","author":"Nakatsu","year":"2023","journal-title":"J. Intell. Syst."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"103196","DOI":"10.1016\/j.ipm.2022.103196","article-title":"A machine learning approach to primacy-peak-recency effect-based satisfaction prediction","volume":"60","author":"Wang","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1016\/j.ijforecast.2015.12.011","article-title":"Cross-validation aggregation for combining autoregressive neural network forecasts","volume":"32","author":"Barrow","year":"2016","journal-title":"Int. J. Forecast."},{"key":"ref_86","first-page":"2079","article-title":"On over-fitting in model selection and subsequent selection bias in performance evaluation","volume":"11","author":"Cawley","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_87","first-page":"133","article-title":"LIUBoost: Locality Informed Under-Boosting for Imbalanced Data Classification","volume":"813","author":"Ahmed","year":"2018","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1186\/s40537-025-01313-4","article-title":"Accuracy, precision, recall, F1-score, or MCC? Empirical evidence from advanced statistics, ML, and XAI for evaluating business predictive models","volume":"12","author":"Sujon","year":"2025","journal-title":"J. Big Data"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Valverde-Albacete, F.J., and Pel\u00e1ez-Moreno, C. (2014). 100% classification accuracy considered harmful: The normalized information transfer factor explains the accuracy paradox. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0084217"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"311","DOI":"10.2753\/MIS0742-1222300311","article-title":"Comprehension and assessment of product reviews: A review\u2013product congruity proposition","volume":"30","author":"Huang","year":"2013","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10055-023-00767-2","article-title":"Recognizing shopper demographics from behavioral responses in a virtual reality store","volume":"27","author":"Guixeres","year":"2023","journal-title":"Virtual Real."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"e2322149121","DOI":"10.1073\/pnas.2322149121","article-title":"Individual differences in human gaze behavior generalize from faces to objects","volume":"121","author":"Broda","year":"2024","journal-title":"Proc. Natl. Acad. Sci. USA"}],"container-title":["Journal of Eye Movement Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1995-8692\/19\/3\/64\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T09:57:03Z","timestamp":1780912623000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1995-8692\/19\/3\/64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,6]]},"references-count":92,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["jemr19030064"],"URL":"https:\/\/doi.org\/10.3390\/jemr19030064","relation":{},"ISSN":["1995-8692"],"issn-type":[{"value":"1995-8692","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,6]]}}}