  Nowadays, personalized recommender systems play an increasingly important
role in music scenarios in our daily life with the preference prediction
ability. However, existing methods mainly rely on users' implicit feedback
(e.g., click, dwell time) which ignores the detailed user experience. This
paper introduces Electroencephalography (EEG) signals to personal music
preferences as a basis for the personalized recommender system. To realize
collection in daily life, we use a dry-electrodes portable device to collect
data. We perform a user study where participants listen to music and record
preferences and moods. Meanwhile, EEG signals are collected with a portable
device. Analysis of the collected data indicates a significant relationship
between music preference, mood, and EEG signals. Furthermore, we conduct
experiments to predict personalized music preference with the features of EEG
signals. Experiments show significant improvement in rating prediction and
preference classification with the help of EEG. Our work demonstrates the
possibility of introducing EEG signals in personal music preference with
portable devices. Moreover, our approach is not restricted to the music
scenario, and the EEG signals as explicit feedback can be used in personalized
recommendation tasks.
