Conference Proceedings

Personalising Mobile Advertising Based on Users’ Installed Apps

Jenna Reps, Uwe Aickelin, Jonathan Garibaldi, Chris Damski

2014 IEEE International Conference on Data Mining Workshop | IEEE | Published : 2014

Abstract

Mobile advertising is a billion pound industry that is rapidly expanding. The success of an advert is measured based on how users interact with it. In this paper we investigate whether the application of unsupervised learning and association rule mining could be used to enable personalised targeting of mobile adverts with the aim of increasing the interaction rate. Over May and June 2014 we recorded advert interactions such as tapping the advert or watching the whole advert video along with the set of apps a user has installed at the time of the interaction. Based on the apps that the users have installed we applied k-means clustering to profile the users into one of ten classes. Due to the ..

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University of Melbourne Researchers