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

机译:个性和分类偏好,以及类别选择对音乐流服务的用户体验的影响

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摘要

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:音乐流服务越来越多地采用不同的方式供用户浏览音乐。除了常用的体裁分类法外,如今还经常使用其他分类法,例如情绪和活动。由于其他分类法已显示能够分散用户的搜索兴趣,因此我们研究了如何预测分类法偏好以解决这一问题。此外,我们研究了分类法中显示的类别数量如何影响用户体验。我们进行了在线用户研究,参与者在其中与名为Tune-A-Find的应用程序进行了交互。在介绍以下两种情况时,我们测量了分类法选择(即情绪,活动或流派),个体差异(例如个性特征和音乐专业知识因素)以及不同的用户体验因素(即选择难度和满意度,感知的系统有用性和质量)所选分类法中的6或24类。在297位参与者中,我们发现人格特质与音乐分类偏好有关。此外,我们的发现表明,分类法中类别的数量以不同的方式影响用户体验,并受音乐专业知识的约束。我们的发现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偏爱的音乐浏览方式,从而缓解当今音乐收听者在通过大量在线音乐选择找到自己的出路时所面临的问题。

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