Learning from the Report-writing Behavior of Individuals
Mohit Kumar, Nikesh Garera, Alexander I. Rudnicky
We describe a briefing system that learns to predict the contents of reports generated by users who create periodic (weekly) reports as part of their normal activity. The system observes content-selection choices that users make and builds a predictive model that could, for example, be used to generate an initial draft report. Using a feature of the interface the system also collects information about potential user-specific features. The system was evaluated under realistic conditions, by collecting data in a project-based university course where student group leaders were tasked with preparing weekly reports for the benefit of the instructors, using the material from individual student reports. This paper addresses the question of whether data derived from the implicit supervision provided by end-users is robust enough to support not only model parameter tuning but also a form of feature discovery. Results indicate that this is the case: system performance improves based on the feedback from user activity. We find that individual learned models (and features) are user-specific, although not completely idiosyncratic. This may suggest that approaches which seek to optimize models globally (say over a large corpus of data) may not in fact produce results acceptable to all individuals.