Several approaches for constructing metrics to capture player experience have been presented previously. In this paper, we propose a generic methodology based on feature selection and preference machine learning for constructing such metric models of the degree to which a player enjoys a given game. For that purpose, previous and new survey experiments on computer and physical interactive games are presented. Given effective data collection a set of numerical features is extracted from a player's interaction with the game and its physiological state. Then feature selection algorithms are employed together with a function approximator based on artificial neural networks to construct feature sets and function that model the players' notion of `fun' for the game under investigation. Performance of the model is evaluated by the degree to which the preferences predicted by the model match those `fun' (entertainment) preferences expressed by the subjects. The results show that effective models can be constructed using the proposed approach. The limitations and the use of the methodology as an effective adaptive mechanism to entertainment augmentation are discussed.