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Personality and taxonomy preferences, and the influence of category choice on the user experience for music streaming services

机译:人格和分类偏好,以及类别选择对音乐流服务用户体验的影响

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Music streaming services increasingly incorporate different ways for users to browse for music. Next to the commonly used genre taxonomy, nowadays additional taxonomies, such as mood and activities, are often used. As additional taxonomies have shown to be able to distract the user in their search, we looked at how to predict taxonomy preferences in order to counteract this. Additionally, we looked at how the number of categories presented within a taxonomy influences the user experience. We conducted an online user study where participants interacted with an application called Tune-A-Find. We measured taxonomy choice (i.e., mood, activity, or genre), individual differences (e.g., personality traits and music expertise factors), and different user experience factors (i.e., choice difficulty and satisfaction, perceived system usefulness and quality) when presenting either 6- or 24-categories within the picked taxonomy. Among 297 participants, we found that personality traits are related to music taxonomy preferences. Furthermore, our findings show that the number of categories within a taxonomy influences the user experience in different ways and is moderated by music expertise. Our findings can support personalized user interfaces in music streaming services. By knowing the user's personality and expertise, the user interface can adapt to the user's preferred way of music browsing and thereby mitigate the problems that music listeners are facing while finding their way through the abundance of music choices online nowadays.
机译:音乐流媒体服务越来越多地包含用户浏览音乐的不同方式。在常用的类型分类旁边,现在常用的额外分类,例如情绪和活动。由于额外的分类系统已经证明能够在搜索中分散用户的注意力,我们研究了如何预测分类偏好以抵消这一点。此外,我们研究了分类学中呈现的类别数量如何影响用户体验。我们进行了在线用户学习,参与者与称为tune-a-find的应用程序互动。我们测量分类学选择(即情绪,活动或类型),个人差异(例如,人格特质和音乐专业是因素),以及不同的用户体验因素(即,选择难度和满足,感知系统用品和质量)挑选分类学中的6个或24类。在297名参与者中,我们发现个性特征与音乐分类偏好有关。此外,我们的研究结果表明,分类学中的类别数量影响了用户体验,以不同的方式受到音乐专业知识的主持。我们的研究结果可以支持音乐流服务中的个性化用户界面。通过了解用户的个性和专业知识,用户界面可以适应用户的首选音乐浏览方式,从而减轻音乐监听器在在线音乐选择的方式面临的问题。

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