Predicting player type in social network games

This paper presents preliminary work towards personalizing and recommending social network games based on a user’s player type. In particular, we present research aimed at supporting such personalization through the prediction of player type from automatically collected user data.
We first provide a brief overview of player types, and then outline several data sources that we gathered from a popular social network game to study the feasibility of player type predictions. Finally, we perform a preliminary analysis using one of these sources, namely music interests.