Event-Driven Interest Detection for Task-Oriented Mobile Apps

Fernando Kaway Carvalho Ota, Farouk Damoun, Sofiane Lagraa, Patricia Becerra-Sánchez, Christophe Atten, Jean Hilger, Radu State

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.