Introducing EEG Analyses to Help Personal Music Preference Prediction

TL;DR

This paper explores using portable EEG signals to improve personalized music preference prediction, demonstrating significant enhancements over traditional implicit feedback methods and highlighting potential for broader recommendation applications.

Contribution

It introduces a novel approach integrating portable EEG data into music preference prediction, showing improved accuracy and establishing feasibility for real-life use.

Findings

EEG signals significantly correlate with music preferences and moods.

EEG-based features improve rating prediction accuracy.

Portable EEG devices enable real-world personalized recommendation systems.

Abstract

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.