Preference Detection Harnessing Low-Cost Portable Electroencephalography and Facial Behavior Markers

EasyChair Preprint 11424

Abstract

Delivering personalized recommendations can improve the effectiveness of user satisfaction. To do this, understanding user preference is critical to developing such recommender systems, however, existing studies mainly utilize high-cost devices and high computation in detecting preference. In this work, we propose a multimodal framework in which facial expressions and neural signals are captured by low-cost portable electroencephalography (EEG) devices in identifying a user’s preference. We found that EEG combined with facial behavior features improves the preference detection, specifically whether a user likes or dislikes the given face images in controlled experiments. Further, we introduce a richer set of objective markers leveraging EEG-based neural features and facial behavior markers that contribute to preference detection. We demonstrate the multimodal-based preference detection using the commercialized portable EEG which can provide an efficient way to approach a user's preference detection in designing personalized recommendation systems in real-world settings.

Keyphrases: EEG-based Neural Signals, Facial Behavior Markers, Preference detection, machine learning, multimodality

@booklet{EasyChair:11424,
  author    = {Sang Won Bae and Feng Liu},
  title     = {Preference Detection Harnessing Low-Cost Portable Electroencephalography and Facial Behavior Markers},
  howpublished = {EasyChair Preprint 11424},
  year      = {EasyChair, 2023}}