Learning from the Report-writing Behavior of Individuals

<p>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.</p> <p>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. Thismay suggest that approaches which seek to optimizemodels globally (say over a large corpus of data) may not in fact produce results acceptable to all individuals.</p>