To tell a story, the storyteller uses all his/her skills to entertain an audience. This task not only relies on the act of telling a story, but also on the ability to understand reactions of the audience during the telling of the story. It is not so difficult to adapt a story for a single individual based on his/her preferences and previous choices. However, the task of choosing what is best for a group becomes quite complicated. The selection by majority voting cannot be effective because it can discard alternatives that are secondary for some individuals, but that would work better for the group in question. Thus, the careless selection of events in a story could cause audience splitting, causing some people to give up keep watching because they were not pleased. This thesis proposes a new methodology to create tailored stories for an audience based on personality traits and preferences of each individual. As an audience may be composed of individuals with similar or mixed preferences, it is necessary to consider a middle ground solution based on the individual options. In addition, individuals may have some kind of relationship with others who influence their decisions. The proposed model addresses all steps in the quest to please the audience. It infers what the preferences are, computes the scenes reward for all individuals, estimates their choices independently and in group, and allows Interactive Storytelling systems to find the story that maximizes the expected audience reward. The proposed model can easily be extended to other areas that involve users interacting with digital environments.