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.

翻译：如今，个性化推荐系统凭借其偏好预测能力，在日常生活的音乐场景中发挥着日益重要的作用。然而，现有方法主要依赖用户的隐式反馈（如点击、停留时间），忽略了用户的具体体验。本文引入脑电图信号作为个性化推荐系统的基础，用于预测个人音乐偏好。为实现日常生活中的数据采集，我们使用干电极便携式设备收集数据。我们开展了一项用户研究，让参与者聆听音乐并记录其偏好和情绪状态，同时通过便携设备采集脑电图信号。对收集数据的分析表明，音乐偏好、情绪与脑电图信号之间存在显著关系。此外，我们基于脑电图信号特征进行了个性化音乐偏好预测实验。实验结果表明，借助脑电图，评分预测和偏好分类的性能得到显著提升。本研究证明了使用便携设备在个人音乐偏好中引入脑电图信号的可行性。此外，我们的方法不仅限于音乐场景，作为显式反馈的脑电图信号还可应用于个性化推荐任务。