A Practical Activity Capture Framework for Personal, Lifetime User Modeling

This paper addresses the problem of capturing rich, longterm personal activity logs of users’ interactions with their workstations, for the purpose of deriving predictive, personal user models. Our architecture addresses a number of practical problems with activity capture, including incorporating heterogeneous information from different applications, measuring phenomena with different rates of change, efficiently scheduling knowledge sources, incrementally evolving knowledge representations, and incorporating prior knowledge to combine low-level observations into interpretations better suited for user modeling tasks. We demonstrate that the computational and memory demands of general activity capture are well within reasonable limits even on today’s hardware and software platforms.