An automated system recommending background music to listen to while working
An automated system recommending background music to listen to while working
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DOI：
10.1007/s11257-022-09325-y
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发表时间：
2022-05
影响因子：
3.6
通讯作者：
Hiromu Yakura;Tomoyasu Nakano;Masataka Goto
中科院分区：
文献类型：
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作者：
Hiromu Yakura;Tomoyasu Nakano;Masataka Goto
现在很多人一边工作一边听音乐。然而，被设计用于播放与用户偏好匹配的歌曲的传统推荐系统不能应用于这种情况。这是因为先前的研究表明，听众的注意力不仅会受到听众强烈不喜欢的音乐的负面影响，而且还会受到听众强烈喜欢的音乐的负面影响。因此，当我们考虑在工作时使用推荐系统时，希望避免用户非常喜欢的歌曲和用户非常不喜欢的歌曲。在此背景下，我们提出了FocusMusicRecommender，一个专门为推荐音乐而设计的系统。它自动总结歌曲并连续播放，以便用户不仅可以通过“跳过”按钮给出“不喜欢（非常）”反馈，还可以通过“继续收听”按钮给出“喜欢（非常）”反馈。然后将反馈与用户的集中度水平相结合，该集中度水平是在相应歌曲的回放期间从用户的行为历史估计的，这允许系统获得区分“喜欢”和“非常喜欢”的偏好信息，而不会给正在工作的用户带来负担。基于偏好信息，系统估计未播放的歌曲的偏好水平，并且还通过考虑用户的当前集中水平来对歌曲进行优先级排序以用于后续回放。实验结果表明了该方法的正确性和有效性，包括浓度水平估计的准确性。此外，我们的用户研究验证了从观察到的行为和获得的评论的参与者的推荐结果的适用性。
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 proposeFocusMusicRecommender, 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.