Transfer learning for cross-game prediction of player experience

Several studies on cross-domain users' behaviour revealed generic personality trails and behavioural patterns. This paper, proposes quantitative approaches to use the knowledge of player behaviour in one game to seed the process of building player experience models in another. We investigate two settings: in the supervised feature mapping method, we use labeled datasets about players' behaviour in two games. The goal is to establish a mapping between the features so that the models build on one dataset could be used on the other by simple feature replacement. For the unsupervised transfer learning scenario, our goal is to find a shared space of correlated features based on unlabelled data. The features in the shared space are then used to construct models for one game that directly work on the transferred features of the other game. We implemented and analysed the two approaches and we show that transferring the knowledge of player experience between domains is indeed possible and ultimately useful when studying players' behaviour and when designing user studies.