An automated system recommending background music to listen to while working

Abstract Many people listen to music while working nowadays. However, conventional recommendation systems that are designed for playing songs matching user preferences cannot be applied for such a situation. This is because previous research showed that listeners’ concentration can be negatively aff...
Ausführliche Beschreibung

Abstract Many people listen to music while working nowadays. However, conventional recommendation systems that are designed for playing songs matching user preferences cannot be applied for such a situation. This is because previous research showed that listeners’ concentration can be negatively affected not only by music that listeners strongly dislike but also by music that the listeners strongly like. Therefore, when we consider a recommendation system to be used while working, it is desirable to avoid both songs the user likes very much and songs the user dislikes very much. Given this background, we propose FocusMusicRecommender, a system designed specifically for recommending music to listen to while working. It summarizes songs automatically and plays them successively in order to enable users to give not only “dislike (very much)” feedback via a “skip” button but also “like (very much)” feedback via a “keep listening” button. The feedback is then combined with the users’ concentration level that is estimated from their behavioral history during the playback of the corresponding song, which allows the system to obtain preference information that distinguishes between “like” and “like very much” without burdening the user who is working. Based on the preference information, the system estimates the preference levels of unplayed songs and prioritizes the songs for subsequent playback by also considering the user’s current concentration level. Our experiments showed the validity and effectiveness of the proposed method, including the accuracy of the concentration level estimation. Moreover, our user study verified the suitability of the recommendation results from both the observed behavior and obtained comments of the participants. Ausführliche Beschreibung