Applying subspace clustering to semi-automate Cooperian persona development based on interview data

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 personalised 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.