Calendar scheduling is a personal behavior and there are diverse factors on which the user’s decision depends. Whether the user is initiating a new meeting or responding to a meeting request she chooses an action with multiple objectives. For instance, when trying to schedule a new meeting at a preferred time and location, the user may also want to minimize change to her existing meetings, and she takes a scheduling action that best compromises the overall objectives. Our goal is to build an agent that can predict the best scheduling action to take, where "best" is defined in terms of the user’s true preference. We take a machine learning approach and focus on the problem of learning the user’s preference, through observation of the user as she engages in meeting scheduling episodes. We propose a hybrid preference learning framework in which we first learn utility functions of simple individual preferences such as preferred time-of-day, and then qualitatively evaluate complex scheduling options by learning a classifier from pairwise preferences. We summarize proof of principal experiments that illustrate both types of learning.