Learning Calendar Scheduling Preferences in Hierarchical Organizations

There has been increasing interest in automating calendar scheduling processes, and in this context, eliciting and reasoning about the user’s scheduling preferences and habits play major roles in finding optimal solutions. In this paper, we present work aimed at learning a user’s time preference for scheduling a meeting. We adopt a passive machine learning approach that observes the user engaging in a series of meeting scheduling episodes with other meeting participants and infers the user’s true preference model from accumulated data. After describing our basic modeling assumptions and approach to learning user preferences, we report the results obtained in an initial set of proof of principle experiments. In these experiments, we use a set of automated CMRADAR calendar scheduling agents to simulate meeting scheduling among a set of users, and use information generated during these interactions as training data for each user’s learner. The learned model of a given user is then evaluated with respect to how well it satisfies that user’s true preference model on a separate set of meeting scheduling tasks. The results show that each learned model is statistically indistinguishable from the true model in their performance with strong confidence, and that the learned model is also significantly better than a random choice model.