FocusMusicRecommender: A System for Recommending Music to Listen to While Working

摘要

This paper proposes FocusMusicRecommender, an automated system recommending background music to listen to while working. Recommendation systems matching user preferences have been widely researched even though research has shown that music that listeners strongly like is not suitable background music because it interferes with their concentration. FocusMusicRecommender plays songs that users may "neither like nor dislike" instead of "like very much." It is designed to by default summarize a song automatically so that users can give "like very much" feedback by pressing a "keep listening" button or "dislike very much" feedback by pressing a "skip" button. It uses this feedback, along with users' concentration levels estimated from their behavior history, to distinguish between the preference levels "like" and "like very much." It then estimates the preference levels of unplayed songs and selects the most suitable song by considering the user's current concentration level. The effectiveness of the proposed feedback method and suitability of the recommendation results were verified experimentally and in user studies. Furthermore, it is confirmed that the proposed method can estimate the user's concentration level more ac-curately than the previous methods.