Machine learning for usability: A case study of mobile application design for Nokia

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2021-12-13
Department
Major/Subject
Human-Computer Interaction and Design
Mcode
SCI3020
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
47+7
Series
Abstract
Nokia launched a website service Customer Insights (CI) to managers and executives from operator companies to track their customers’ experience. An upgraded mobile service is developed for providing more valuable information. The data was retrieved from the same dataset but less amount of information would be displayed in the mobile application. Two questions need to be answered in this design work, what to show in the application and how to show them. A tough situation in user research and a large amount of data made the user-centered design hard to answer the ‘what’ question. Based on experts’ views, data points that have different patterns from other data could be valuable. Considering ML is good at quantitative analysis tools and anomaly detection method can help filter outliers, we combined it with User-centered Design (UCD) in the content preparation. The challenge was how to mind the gap between experts and real users’ expectations. The initial user research was missed and involving users during the modeling progress was not realistic. Our strategy was to select information by anomaly detection methods, got users’ feedbacks after launching the application and utilized those feedbacks to improve the algorithm. Based on the study in ML, PCA anomaly detection was chosen and it worked well in filtering outliers in this case. Two validations proved the possibility of improving the precision and recall of the results based on supervised learning and labeled data. On the other hand, UCD focused on answering the ‘how’ problem based on a questionnaire, personas, scenarios and design guidelines. The results from ML research were also considered in the design work, thus the interface and interaction design would help the algorithm to a larger extent. Four experts participated in the design evaluation. All three iterations of the design helped us to summarize some universal guidance on how to design for similar mobile applications.
Description
Supervisor
Hedman, Anders
Thesis advisor
Li, Haibo
Keywords
machine learning, user-centered design, anomaly detection, user experience
Other note
Citation