  Advanced music recommendation systems are being introduced along with the
development of machine learning. However, it is essential to design a music
recommendation system that can increase user satisfaction by understanding
users' music tastes, not by the complexity of models. Although several studies
related to music recommendation systems exploiting negative preferences have
shown performance improvements, there was a lack of explanation on how they led
to better recommendations. In this work, we analyze the role of negative
preference in users' music tastes by comparing music recommendation models with
contrastive learning exploiting preference (CLEP) but with three different
training strategies - exploiting preferences of both positive and negative
(CLEP-PN), positive only (CLEP-P), and negative only (CLEP-N). We evaluate the
effectiveness of the negative preference by validating each system with a small
amount of personalized data obtained via survey and further illuminate the
possibility of exploiting negative preference in music recommendations. Our
experimental results show that CLEP-N outperforms the other two in accuracy and
false positive rate. Furthermore, the proposed training strategies produced a
consistent tendency regardless of different types of front-end musical feature
extractors, proving the stability of the proposed method.
