Existing research on music recommendation systems primarily focuses on recommending similar music, thereby often neglecting diverse and distinctive musical recordings. Musical outliers can provide valuable insights due to the inherent diversity of music itself. In this paper, we explore music outliers, investigating their potential usefulness for music discovery and recommendation systems. We argue that not all outliers should be treated as noise, as they can offer interesting perspectives and contribute to a richer understanding of an artist's work. We introduce the concept of 'Genuine' music outliers and provide a definition for them. These genuine outliers can reveal unique aspects of an artist's repertoire and hold the potential to enhance music discovery by exposing listeners to novel and diverse musical experiences.

翻译：现有音乐推荐系统研究主要聚焦于推荐相似音乐，往往忽视了多样且独特的音乐录音。音乐离群点因其本身的内在多样性，能提供有价值的洞见。本文对音乐离群点进行探索，研究其在音乐发现和推荐系统中的潜在效用。我们认为，不应将所有离群点都视为噪声，因为它们能提供有趣的视角，有助于更丰富地理解艺术家的作品。我们提出"真正"音乐离群点的概念并给出定义。这些真正离群点能揭示艺术家曲目中的独特方面，通过让听众接触新颖多样的音乐体验，具有增强音乐发现的潜力。