<reponame>cbjuan/juancb.es<gh_stars>0
@article{8240912,
 abstract = {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.},
 author = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>},
 doi = {10.1109/ACCESS.2017.2782678},
 issn = {2169-3536},
 journal = {IEEE Access},
 keywords = {human factors;learning (artificial intelligence);user interfaces;enabling adaptability;user characteristics detection;machine learning;original study;questionnaires;machine-learning procedures;A/B testing approach;user preferences;optimal version;predictive models;machine-learning algorithms;relevant factors;similar characteristics;different versions;adequate version;user interface;user experience;redirection rules;Web forms;user behavior;heuristic rules;Predictive models;Testing;Employment;Tools;Machine learning algorithms;Software;Electronic mail;Adaptability;machine learning;user profiles;web forms;clusters;hierarchical clustering;random forest;A/B testing;human-computer interaction;HCI},
 month = {},
 number = {},
 pages = {2251-2265},
 title = {Enabling Adaptability in Web Forms Based on User Characteristics Detection Through A/B Testing and Machine Learning},
 volume = {6},
 year = {2018}
}

