Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/323578 
Year of Publication: 
2025
Citation: 
[Journal:] Electronic Markets [ISSN:] 1422-8890 [Volume:] 35 [Issue:] 1 [Article No.:] 37 [Publisher:] Springer Berlin Heidelberg [Place:] Berlin/Heidelberg [Year:] 2025
Publisher: 
Springer Berlin Heidelberg, Berlin/Heidelberg
Abstract: 
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-computer interfaces and hybrid intelligence devices enable access to human brains, using electroencephalographic brain data becomes more relevant in future. Contrary to the majority view of previous research, our results show that there is a link between personality traits and brain features of a user. With a four times higher probability of correctly predicting the personality of an independent user compared to naive prediction, we demonstrate the possibility of predicting a user’s personality based on their brain information and thus showing a new reliable approach for marketing purposes in electronic markets.
Subjects: 
Convolutional neural network
Predictive analysis
Five-factor model
Machine learning
Personality mining
Resting-state electroencephalogram
Persistent Identifier of the first edition: 
Additional Information: 
C89;C90;D40;M31;M37
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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