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}.

翻译：推荐系统已成为音乐流媒体服务中不可或缺的一部分，通过个性化播放列表和促进新音乐的偶然发现，显著提升了用户体验。然而，现有推荐系统忽视了音乐领域特有的挑战，特别是随机播放模式——该模式以随机顺序提供后续曲目。基于我们的观察发现：随机播放会话因具有极高的唯一跳转率，主要阻碍了音乐推荐系统的整体训练过程，我们提出了面向随机播放推荐增强的音乐推荐系统（MUSE）。MUSE采用自监督学习框架，最大化原始会话与增强会话之间的一致性，其中增强会话通过我们新提出的会话增强方法——基于跳转的数据增强来实现。为进一步促进两个视图间表示的语义对齐，我们设计了两种细粒度匹配策略，即基于项目的匹配策略和基于相似度的匹配策略。通过在多样化环境下进行的严谨实验，我们证明了MUSE在来自Spotify的大规模音乐流媒体会话数据集（MSSD）上，相较于12种基线模型具有更优性能。MUSE的源代码可在\url{https://github.com/yunhak0/MUSE}获取。