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Online music services are increasing in popularity. They enable us to analyzepeople's music listening behavior based on play logs. Although it is known thatpeople listen to music based on topic (e.g., rock or jazz), we assume that whena user is addicted to an artist, s/he chooses the artist's songs regardless oftopic. Based on this assumption, in this paper, we propose a probabilisticmodel to analyze people's music listening behavior. Our main contributions arethree-fold. First, to the best of our knowledge, this is the first studymodeling music listening behavior by taking into account the influence ofaddiction to artists. Second, by using real-world datasets of play logs, weshowed the effectiveness of our proposed model. Third, we carried outqualitative experiments and showed that taking addiction into account enablesus to analyze music listening behavior from a new viewpoint in terms of howpeople listen to music according to the time of day, how an artist's songs arelistened to by people, etc. We also discuss the possibility of applying theanalysis results to applications such as artist similarity computation and songrecommendation.

Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation