Interest detection; Behaviour modelling; High utility events
Abstract :
[en] 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.
Disciplines :
Computer science
Author, co-author :
Ota, Fernando Kaway Carvalho
DAMOUN, Farouk ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
Lagraa, Sofiane
Becerra-Sanchez, Patricia
HILGER, Jean ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SnT Finnovation Hub
STATE, Radu ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
External co-authors :
yes
Language :
English
Title :
Event-Driven Interest Detection for Task-Oriented Mobile Apps
Publication date :
2022
Event name :
18th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
Event place :
Beppu, Japan
Event date :
November 8-11, 2021
By request :
Yes
Audience :
International
Main work title :
Mobile and Ubiquitous Systems: Computing, Networking and Services
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