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UM
2007
Springer

Principles of Lifelong Learning for Predictive User Modeling

10 years 4 months ago
Principles of Lifelong Learning for Predictive User Modeling
Predictive user models often require a phase of effortful supervised training where cases are tagged with labels that represent the status of unobservable variables. We formulate and study principles of lifelong learning where training is ongoing over a prolonged period. In lifelong learning, decisions about extending a case library are made continuously by balancing the cost of acquiring values of hidden states with the long-term benefits of acquiring new labels. We highlight key principles by extending BusyBody, an application that learns to predict the cost of interrupting a user. We transform the prior BusyBody system into a lifelong learner and then review experiments that highlight the promise of the methods.
Ashish Kapoor, Eric Horvitz
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where UM
Authors Ashish Kapoor, Eric Horvitz
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