  Recommender systems have become indispensable in music streaming services,
enhancing user experiences by personalizing playlists and facilitating the
serendipitous discovery of new music. However, the existing recommender systems
overlook the unique challenges inherent in the music domain, specifically
shuffle play, which provides subsequent tracks in a random sequence. Based on
our observation that the shuffle play sessions hinder the overall training
process of music recommender systems mainly due to the high unique transition
rates of shuffle play sessions, we propose a Music Recommender System with
Shuffle Play Recommendation Enhancement (MUSE). MUSE employs the
self-supervised learning framework that maximizes the agreement between the
original session and the augmented session, which is augmented by our novel
session augmentation method, called transition-based augmentation. To further
facilitate the alignment of the representations between the two views, we
devise two fine-grained matching strategies, i.e., item- and similarity-based
matching strategies. Through rigorous experiments conducted across diverse
environments, we demonstrate MUSE's efficacy over 12 baseline models on a
large-scale Music Streaming Sessions Dataset (MSSD) from Spotify. The source
code of MUSE is available at \url{https://github.com/yunhak0/MUSE}.
