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Creating user stereotypes for persona development from qualitative data through semi-automatic subspace clustering

机译：通过定性数据通过半自动子空间聚类为角色开发创建用户构造型

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摘要

Personas are models of users that incorporate motivations, wishes, and objectives; These models are employed in user-centred design to help design better user experiences and have recently been employed in adaptive systems to help tailor the personalized user experience. Designing with personas involves the production of descriptions of fictitious users, which are often based on data from real users. The majority of data-driven persona development performed today is based on qualitative data from a limited set of interviewees and transformed into personas using labour-intensive manual techniques. In this study, we propose a method that employs the modelling of user stereotypes to automate part of the persona creation process and addresses the drawbacks of the existing semi-automated methods for persona development. The description of the method is accompanied by an empirical comparison with a manual technique and a semi-automated alternative (multiple correspondence analysis). The results of the comparison show that manual techniques differ between human persona designers leading to different results. The proposed algorithm provides similar results based on parameter input, but was more rigorous and will find optimal clusters, while lowering the labour associated with finding the clusters in the dataset. The output of the method also represents the largest variances in the dataset identified by the multiple correspondence analysis.
机译：角色是结合动机，愿望和目标的用户模型；这些模型被用于以用户为中心的设计中，以帮助设计更好的用户体验，并且最近被用于自适应系统中，以帮助定制个性化的用户体验。使用角色进行设计涉及生成虚拟用户的描述，这些描述通常基于真实用户的数据。今天进行的大多数由数据驱动的角色开发都是基于来自少数受访者的定性数据，并使用劳动密集型手动技术将其转化为角色。在这项研究中，我们提出了一种方法，该方法利用用户原型的建模来自动化角色创建过程的一部分，并解决现有的半自动化角色开发方法的弊端。该方法的描述伴随有与手动技术和半自动替代方法（多次对应分析）的经验比较。比较的结果表明，人类角​​色设计者之间的手动技术有所不同，从而导致了不同的结果。所提出的算法基于参数输入提供了相似的结果，但是更加严格，可以找到最佳的聚类，同时降低了与在数据集中寻找聚类相关的工作量。该方法的输出还表示通过多重对应分析确定的数据集中的最大方差。

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