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Effects of personal characteristics in control-oriented user interfaces for music recommender systems

机译:个人特征在控制型用户界面中的音乐推荐系统的影响

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Music recommender systems typically offer a "one-size-fits-all" approach with the same user controls and visualizations for all users. However, the effectiveness of interactive interfaces for music recommender systems is likely to be affected by individual differences. In this paper, we first conduct a comprehensive literature review of interactive interfaces in recommender systems to motivate the need for personalized interaction with music recommender systems, and two personal characteristics, visual memory and musical sophistication. More specifically, we studied the influence of these characteristics on the design of (a) visualizations for enhancing recommendation diversity and (b) the optimal level of user controls while minimizing cognitive load. The results of three experiments show a benefit for personalizing both visualization and control elements to musical sophistication. We found that (1) musical sophistication influenced the acceptance of recommendations for user controls. (2) musical sophistication also influenced recommendation acceptance, and perceived diversity for visualizations and the UI combining user controls and visualizations. However, musical sophistication only strengthens the impact of UI on perceived diversity (moderation effect) when studying the combined effect of controls and visualizations. These results allow us to extend the model for personalization in music recommender systems by providing guidelines for interactive visualization design for music recommender systems, with regard to both visualizations and user control.
机译:音乐推荐系统通常提供“单尺寸适合所有”方法,对所有用户具有相同的用户控件和可视化。然而,音乐推荐系统的交互式接口的有效性可能受到个体差异的影响。在本文中,我们首先对推荐系统中的交互式接口进行了全面的文献综述,以激励与音乐推荐系统的个性化互动的需求,以及两个个人特征,视觉记忆和音乐复杂性。更具体地说,我们研究了这些特征对(a)可视化设计的影响,用于增强推荐分集和(b)最佳用户控制的最佳水平,同时最小化认知负载。三个实验的结果表明了个性化可视化和控制元素的益处,以音乐复杂。我们发现(1)音乐精致影响了对用户控件的建议接受。 (2)音乐复杂性也影响了推荐接受,并感知可视化和UI组合用户控件和可视化的多样性。然而,在研究控制和可视化的组合效果时,音乐复杂性仅加强UI对感知多样性(适度效应)的影响。这些结果允许我们通过为既可视化和用户控制提供音乐推荐系统的交互式可视化设计指南,扩展音乐推荐系统中的个性化模型。

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