{"id":319169,"date":"2016-11-10T10:42:59","date_gmt":"2016-11-10T18:42:59","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=319169"},"modified":"2018-10-16T20:14:56","modified_gmt":"2018-10-17T03:14:56","slug":"experience-sampling-building-predictive-user-models-comparative-study","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/experience-sampling-building-predictive-user-models-comparative-study\/","title":{"rendered":"Experience Sampling for Building Predictive User Models: A Comparative Study"},"content":{"rendered":"

Experience sampling has been employed for decades to collect assessments of subjects\u2019 intentions, needs, and affective states. In recent years, investigators have employed automated experience sampling to collect data to build predictive user models. To date, most procedures have relied on random sampling or simple heuristics. We perform a comparative analysis of several automated strategies for guiding experience sampling, spanning a spectrum of sophistication, from a random sampling procedure to increasingly sophisticated active learning. The more sophisticated methods take a decision-theoretic approach, centering on the computation of the expected value of information of a probe, weighing the cost of the short-term disruptiveness of probes with their benefits in enhancing the long-term performance of predictive models. We test the different approaches in a field study, focused on the task of learning predictive models of the cost of interruption.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Experience sampling has been employed for decades to collect assessments of subjects\u2019 intentions, needs, and affective states. In recent years, investigators have employed automated experience sampling to collect data to build predictive user models. To date, most procedures have relied on random sampling or simple heuristics. 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