Abstract
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.
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Kapoor, A., Horvitz, E. (2007). Principles of Lifelong Learning for Predictive User Modeling. In: Conati, C., McCoy, K., Paliouras, G. (eds) User Modeling 2007. UM 2007. Lecture Notes in Computer Science(), vol 4511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73078-1_7
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DOI: https://doi.org/10.1007/978-3-540-73078-1_7
Publisher Name: Springer, Berlin, Heidelberg
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