Calendar Assistants that Learn Preferences.
Jean Oh, Stephen F. Smith · 2005
Calendar scheduling is a personal behavior and there are di-verse factors on which the user’s decision depends. Whether the user is initiating a new meeting or responding to a meet-ing request she chooses an action with multiple objectives. For instance, when trying to schedule a new meeting at a pre-ferred 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 ob-servation of the user as she engages in meeting scheduling episodes. We propose a hybrid preference learning frame-work in which we first learn utility functions of simple in-dividual 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.