Imperial College London

Professor Emil Lupu

Faculty of EngineeringDepartment of Computing

Professor of Computer Systems



e.c.lupu Website




564Huxley BuildingSouth Kensington Campus






BibTex format

author = {Markitanis, A and Corapi, D and Russo, A and Lupu, EC},
booktitle = {Latest Advances in Inductive Logic Programming},
doi = {10.1142/9781783265091_0006},
pages = {43--51},
title = {Learning user behaviours in real mobile domains},
url = {},
year = {2014}

RIS format (EndNote, RefMan)

AB - © 2015 Imperial College Press. All rights reserved. With the emergence of ubiquitous computing, innovations in mobile phones are increasingly changing the way users lead their lives. To make mobile devices adaptive and able to autonomously respond to changes in user behaviours, machine learning techniques can be deployed to learn behaviour from empirical data. Learning outcomes should be rulebased enforcement policies that can pervasively manage the devices, and at the same time facilitate user validation when and if required. In this chapter we demonstrate the feasibility of non-monotonic Inductive Logic Programming (ILP) in the automated task of extraction of user behaviour rules through data acquisition in the domain of mobile phones. This is a challenging task as real mobile datasets are highly noisy and unevenly distributed. We present two applications, one based on an existing dataset collected as part of the Reality Mining group, and the other generated by a mobile phone application called ULearn that we have developed to facilitate a realistic evaluation of the accuracy of the learning outcome.
AU - Markitanis,A
AU - Corapi,D
AU - Russo,A
AU - Lupu,EC
DO - 10.1142/9781783265091_0006
EP - 51
PY - 2014///
SN - 9781783265084
SP - 43
TI - Learning user behaviours in real mobile domains
T1 - Latest Advances in Inductive Logic Programming
UR -
ER -