Creating user stereotypes for persona development from qualitative data
through semi-automatic subspace clustering
- URL: http://arxiv.org/abs/2306.14551v1
- Date: Mon, 26 Jun 2023 09:49:51 GMT
- Title: Creating user stereotypes for persona development from qualitative data
through semi-automatic subspace clustering
- Authors: Dannie Korsgaard, Thomas Bjorner, Pernille Krog Sorensen, Paolo
Burelli
- Abstract summary: We propose a method that employs the modelling of user stereotypes to automate part of the persona creation process.
Results 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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