Cross-Game Modeling of Player's Behaviour in Free-To-Play Games

Abstract

Player modelling is an important task for almost any game creator, which helps in understanding the player-base. One of the major issues is an early leave of players which makes modelling them challenging. In our research, we focus on the cold-start problem by utilizing information about a player from multiple games or other players in a given game. Although multiple studies focus on cross-game modelling, they still often require manual mapping of features or don't consider a player's behaviour specific to the given game. Our proposed method is based on transfer learning and unsupervised translation. In addition, we propose a combination of group-based and individual player models.