  Music has the power to evoke intense emotional experiences and regulate the
mood of an individual. With the advent of online streaming services, research
in music recommendation services has seen tremendous progress. Modern methods
leveraging the listening histories of users for session-based song
recommendations have overlooked the significance of features extracted from
lyrics and acoustic content. We address the task of song prediction through
multiple modalities, including tags, lyrics, and acoustic content. In this
paper, we propose a novel deep learning approach by refining Attentive Neural
Networks using representations derived via a Transformer model for lyrics and
Variational Autoencoder for acoustic features. Our model achieves significant
improvement in performance over existing state-of-the-art models using lyrical
and acoustic features alone. Furthermore, we conduct a study to investigate the
impact of users' psychological health on our model's performance.
