Journal
IEEE ACCESS
Volume 6, Issue -, Pages 2251-2265Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2782678
Keywords
Adaptability; machine learning; user profiles; web forms; clusters; hierarchical clustering; random forest; A/B testing; human-computer interaction; HCI
Categories
Funding
- la Caixa Foundation
- Spanish Government Ministry of Economy and Competitiveness throughout the DEFINES Project [TIN2016-80172-R]
- University of Salamanca through the Program of Financial Aid for the Predoctoral Contract of Jose Carlos Sanchez-Prieto (Programa III: Ayudas para contratos Predoctorales)
- European Social Fund
- Consejerla de Educacion of the Junta de Castilla y Leon, Spain
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This paper presents an original study with the aim of improving users' performance in completing large questionnaires through adaptability in web forms. Such adaptability is based on the application of machine-learning procedures and an A/B testing approach. To detect the user preferences, behavior, and the optimal version of the forms for all kinds of users, researchers built predictive models using machine-learning algorithms (trained with data from more than 3000 users who participated previously in the questionnaires), extracting the most relevant factors that describe the models, and clustering the users based on their similar characteristics and these factors. Based on these groups and their performance in the system, the researchers generated heuristic rules between the different versions of the web forms to guide users to the most adequate version (modifying the user interface and user experience) for them. To validate the approach and confirm the improvements, the authors tested these redirection rules on a group of more than 1000 users. The results with this cohort of users were better than those achieved without redirection rules at the initial stage. Besides these promising results, the paper proposes a future study that would enhance the process (or automate it) as well as push its application to other fields.
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