PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms


  • Tiancheng Shen Tsinghua University
  • Jia Jia Tsinghua University
  • Yan Li Tencent Wechat AI
  • Yihui Ma Tsinghua University
  • Yaohua Bu Tsinghua University
  • Hanjie Wang Tencent Wechat AI
  • Bo Chen Tencent Wechat AI
  • Tat-Seng Chua National University of Singapore
  • Wendy Hall University of Southampton



With the rapid expansion of digital music formats, it's indispensable to recommend users with their favorite music. For music recommendation, users' personality and emotion greatly affect their music preference, respectively in a long-term and short-term manner, while rich social media data provides effective feedback on these information. In this paper, aiming at music recommendation on social media platforms, we propose a Personality and Emotion Integrated Attentive model (PEIA), which fully utilizes social media data to comprehensively model users' long-term taste (personality) and short-term preference (emotion). Specifically, it takes full advantage of personality-oriented user features, emotion-oriented user features and music features of multi-faceted attributes. Hierarchical attention is employed to distinguish the important factors when incorporating the latent representations of users' personality and emotion. Extensive experiments on a large real-world dataset of 171,254 users demonstrate the effectiveness of our PEIA model which achieves an NDCG of 0.5369, outperforming the state-of-the-art methods. We also perform detailed parameter analysis and feature contribution analysis, which further verify our scheme and demonstrate the significance of co-modeling of user personality and emotion in music recommendation.




How to Cite

Shen, T., Jia, J., Li, Y., Ma, Y., Bu, Y., Wang, H., Chen, B., Chua, T.-S., & Hall, W. (2020). PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 206-213.



AAAI Technical Track: AI and the Web