  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 large number of apps considered we implemented dimension reduction to
reduced the app feature space by mapping the apps to their iTunes category and
clustered users based on the percentage of their apps that correspond to each
iTunes app category. The clustering was externally validated by investigating
differences between the way the ten profiles interact with the various adverts
genres (lifestyle, finance and entertainment adverts). In addition association
rule mining was performed to find whether the time of the day that the advert
is served and the number of apps a user has installed makes certain profiles
more likely to interact with the advert genres. The results showed there were
clear differences in the way the profiles interact with the different advert
genres and the results of this paper suggest that mobile advert targeting would
improve the frequency that users interact with an advert.
