Research Article
Event-Driven Interest Detection for Task-Oriented Mobile Apps
4 downloads
@INPROCEEDINGS{10.1007/978-3-030-94822-1_38, author={Fernando Kaway Carvalho Ota and Farouk Damoun and Sofiane Lagraa and Patricia Becerra-Sanchez and Christophe Atten and Jean Hilger and Radu State}, title={Event-Driven Interest Detection for Task-Oriented Mobile Apps}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 18th EAI International Conference, MobiQuitous 2021, Virtual Event, November 8-11, 2021, Proceedings}, proceedings_a={MOBIQUITOUS}, year={2022}, month={2}, keywords={Interest detection Behaviour modelling High utility events}, doi={10.1007/978-3-030-94822-1_38} }- Fernando Kaway Carvalho Ota
Farouk Damoun
Sofiane Lagraa
Patricia Becerra-Sanchez
Christophe Atten
Jean Hilger
Radu State
Year: 2022
Event-Driven Interest Detection for Task-Oriented Mobile Apps
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-030-94822-1_38
Abstract
Mobile applications became the main interaction channel in several domains, such as banking. Consequently, understanding user behaviour on those apps has drawn attention in order to extract business-oriented outcomes. By combining Markov Chain and graph theory techniques, we successfully developed a process to model the app, to extract the click high utility events, to score the interest on those events and cluster the groups of interest. We tested our approach on an European bank dataset with over 3.5 millions of user’s session. By implementing our approach, analysts can gain knowledge of user behaviour in terms of events that are important to the domain.
Copyright © 2021–2025 ICST
