An interactive narrative is a form of digital entertainment in which players can create or influence a dramatic storyline through actions, typically by assuming the role of a character in a fictional virtual world. The interactive narrative systems usually employ a drama manager (DM), an omniscient background agent that monitors the fictional world and determines what will happen next in the players' story experience. Prevailing approaches to drama management choose successive story plot points based on a set of criteria given by the game designers. In other words, the DM is a surrogate for the game designers. In this dissertation, I create a data-driven personalized drama manager that takes into consideration players' preferences. The personalized drama manager is capable of (1) modeling the players' preference over successive plot points from the players' feedback; (2) guiding the players towards selected plot points without sacrificing players' agency; (3) choosing target successive plot points that simultaneously increase the player's story preference ratings and the probability of the players selecting the plot points. To address the first problem, I develop a collaborative filtering algorithm that takes into account the specific sequence (or history) of experienced plot points when modeling players' preferences for future plot points. Unlike the traditional collaborative filtering algorithms that make one-shot recommendations of complete story artifacts (e.g., books, movies), the collaborative filtering algorithm I develop is a sequential recommendation algorithm that makes every successive recommendation based on all previous recommendations. To address the second problem, I create a multi-option branching story graph that allows multiple options to point to each plot point. The personalized DM working in the multi-option branching story graph can influence the players to make choices that coincide with the trajectories selected by the DM, while gives the players the full agency to make any selection that leads to any plot point in their own judgement. To address the third problem, the personalized DM models the probability that the players transitioning to each full-length stories and selects target stories that achieve the highest expected preference ratings at every branching point in the story space. The personalized DM is implemented in an interactive narrative system built with choose-your-own-adventure stories. Human study results show that the personalized DM can achieve significantly higher preference ratings than non-personalized DMs or DMs with pre-defined player types, while preserve the players' sense of agency.