SiTunes: A Situational Music Recommendation Dataset with Physiological and Psychological Signals

Vadim Grigorev, Jiayu Li, Weizhi Ma, Zhiyu He, Min Zhang, Yiqun Liu, Ming Yan, Ji Zhang

Abstract: With an increasing number of music tracks available online, music recommender systems have become popular and ubiquitous. Previous research indicates that people’s preferences, especially in music, dynamically change with various factors, such as surrounding situations and emotional status. However, few existing public recommendation datasets contain such situation or emotion information. Therefore, we constructed SiTunes, a situational music recommendation dataset with rich physiological and psychological signals. We collected the data through a three-stage user study, including: (1) recorded users’ inherent music preference in a lab setting (Stage 1), (2) recorded physiological and environmental situations by smart wristband devices in users’ daily life, and provided psychological and rating feedback for music recommended by traditional recommenders (Stage 2) and (3) by situation-aware recommenders (Stage 3). The experiments were conducted with strict privacy concerns and ethical approval. The dataset contains over 2000 listening logs from 30 users on over 300 music tracks. SiTunes serves as a valuable resource for future studies on situational recommenders and user understanding in recommendation. The dataset is available at https://github.com/JiayuLi-997/SiTunes_dataset/.