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.
