  Interpretation of retrieved results is an important issue in music
recommender systems, particularly from a user perspective. In this study, we
investigate the methods for providing interpretability of content features
using self-attention. We extract lyric features with the self-attentive genre
classification model trained on 140,000 tracks of lyrics. Likewise, we extract
acoustic features using the acoustic model with self-attention trained on
120,000 tracks of acoustic signals. The experimental results show that the
proposed methods provide the characteristics that are interpretable in terms of
both lyrical and musical contents. We demonstrate this by visualizing the
attention weights, and by presenting the most similar songs found using lyric
or audio features.
