Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems

2021
Oksana Ņikiforova, Vitālijs Zabiņako, Jurijs Korņijenko, Madara Gasparoviča-Asīte, Amanda Siliņa

Improving IS (Information System) end-user experience is one of the most important tasks in the analysis of end-users behaviour, evaluation and identification of its improvement potential. However, the application of Machine Learning methods for the UX (User Experience) usability and efficiency improvement is not widely researched. In the context of the usability analysis, the information about behaviour of end-users could be used as an input, while in the output data the focus should be made on non-trivial or difficult attention-grabbing events and scenarios. The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define dependency between these data and desired output, which can help to apply Machine Learning / graph algorithms to user activity records.

Atslēgas vārdi
Data mapping, IS usability, Machine Learning, User Experience (UX).
DOI
10.2478/acss-2021-0003
Hipersaite
https://www.sciendo.com/article/10.2478/acss-2021-0003

Ņikiforova, O., Zabiņako, V., Korņijenko, J., Gasparoviča-Asīte, M., Siliņa, A. Mapping of Source and Target Data for Application to Machine Learning Driven Discovery of IS Usability Problems. Applied Computer Systems, 2021, Vol. 26, No. 1, 22.-30. lpp. ISSN 2255-8683. e-ISSN 2255-8691. Pieejams: doi:10.2478/acss-2021-0003

Publikācijas valoda
English (en)