딥러닝 기반 뉴로사이언스 마이닝 기법을 이용한 고객 매력/유용성 인지 (CAUP) 예측 성능에 관한 탐색적 연구: Dark vs Light 사용자 인터페이스 (UI)를 중심으로

Exploring the Performance of Deep Learning-Driven Neuroscience Mining in Predicting CAUP (Consumer's Attractiveness/Usefulness Perception): Emphasis on Dark vs Light UI Modes

In this work, we studied consumers' attractiveness/usefulness perceptions (CAUP) of online commerce product photos when exposed to alternative dark/light user interface (UI) modes. We analyzed time-series EEG data from 31 individuals and performed neuroscience mining (NSM) to ascertain (a) how the CAUP of products differs among UI modes; and (b) which deep learning model provides the most accurate assessment of such neuroscience mining (NSM) business difficulties. The dark UI style increased the CAUP of the products displayed and was predicted with the greatest accuracy using a unique EEG power spectra separated wave brainwave 2D-ConvLSTM model. Then, using relative importance analysis, we used this model to determine the most relevant power spectra. Our findings are considered to contribute to the discovery of objective truths about online customers' reactions to various user interface modes used by various online marketplaces that cannot be uncovered through more traditional research approaches like as surveys.