Personality and Preference Modeling for Adaptive Storytelling

In almost all forms of storytelling, the background and the current state of mind of the audience members predispose them to experience a given story from a unique personal perspective. However, traditional story writers usually construct their narratives based on an average understanding of the preferences of their audience, which does not guarantee satisfying narrative experiences for its individual members. When a narrative is aimed at providing pleasurable entertainment, having some information about the preferences of the current user for the narrative’s content is vital to create satisfying experiences. This paper explores personality modeling and proposes a novel approach to generate individualized interactive narratives based on the preferences of users, which are modeled in terms of the Big Five factors. The paper presents the proposed method and evaluates its precision and real-time performance.