Online music services are increasing in popularity. They enable us to analyze
people's music listening behavior based on play logs. Although it is known that
people listen to music based on topic (e.g., rock or jazz), we assume that when
a user is addicted to an artist, s/he chooses the artist's songs regardless of
topic. Based on this assumption, in this paper, we propose a probabilistic
model to analyze people's music listening behavior. Our main contributions are
three-fold. First, to the best of our knowledge, this is the first study
modeling music listening behavior by taking into account the influence of
addiction to artists. Second, by using real-world datasets of play logs, we
showed the effectiveness of our proposed model. Third, we carried out
qualitative experiments and showed that taking addiction into account enables
us to analyze music listening behavior from a new viewpoint in terms of how
people listen to music according to the time of day, how an artist's songs are
listened to by people, etc. We also discuss the possibility of applying the
analysis results to applications such as artist similarity computation and song
recommendation.

本文提出一种基于艺术家成瘾影响的概率模型，以分析人们的音乐收听行为，并使用实际数据集展示了该模型的有效性，进而从新的视角分析了人们如何根据一天的时间以及艺术家的歌曲收听情况来听音乐，探讨了应用分析结果的可能性，如相似艺术家订阅和推荐歌曲等。