Significant research has been conducted on human decision making behavior in recommendation systems during the past decade, yet it remains a challenge to design effective and efficient recommendation systems so that they not only produce useful suggestions and ease the decision making task but also turn it into a pleasurable experience. Algorithms have been designed based on research that highlight individual theoretical constructs yet there is an absence of a comprehensive model of human decision-making. This research offers an insight into the core of this issue by examining the neural correlates of human decision-making using Electroencephalography (EEG). The insights generated maybe used to construct a comprehensive model of human decision making in recommendation systems and generate new design principles for the same.
CITATION STYLE
Quazilbash, N. Z., Asif, Z., & Naqvi, S. A. A. (2019). Neural correlates of human decision making in recommendation systems: A research proposal. In Lecture Notes in Information Systems and Organisation (Vol. 29, pp. 139–145). Springer Heidelberg. https://doi.org/10.1007/978-3-030-01087-4_17
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