A novel subject-independent deep learning approach for user behavior prediction in electronic markets based on electroencephalographic data

• Published in: Electronic markets Vol. 35; no. 1; p. 37
• Main Authors: Penava, Pascal, Buettner, Ricardo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025; Springer Nature B.V
• Subjects: Automation; Brain; Brain research; Business and Management; Convolutional neural network; Customer relationship management; Deep learning; e-Commerce/e-business; Electroencephalography; Five-factor model; Human-computer interface; Hypotheses; Intelligence; Interfaces; IT in Business; Learning; Machine learning; Marketing; Markets; Neural networks; Personality; Personality mining; Personality traits; Physiology; Predictions; Predictive analysis; Predictive analytics; Recommender systems; Research Paper; Resting-state electroencephalogram; Social media; Social networks; User behavior; M37; Five-factor model; D40; Resting-state electroencephalogram; C89; Convolutional neural network; Machine learning; Personality mining; Predictive analysis; M31; C90

Abstract Based on the work by Buettner (2017) showing a personality-based recommender system for electronic markets using social media data, we extend the work by proposing a novel deep learning-based engine to predict the user’s personality just based on electroencephalographic brain data. As brain...