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.

本研究提出了一种使用用户刻板印象模型来自动化部分人设创建过程并解决现有半自动化方法的缺点的方法，并通过实证比较手动技术和半自动化技术的结果表明，这种提出的算法提供了相似的结果，并且更严谨，会发现数据集中的最佳聚类，同时降低与数据集聚类相关的人力成本。

利用半自动子空间聚类从定性数据中创建用户刻板形象以进行角色开发