Abstract:
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Item Response Theory (IRT) models are traditionally used in educational and psychological measurement contexts where the item parameters are estimated from a sample of data and applied to score participant responses. We discuss how this modeling framework can be applied to extremely large volumes of data and real-time decision making as a parametric machine-learning approach. We highlight some of the challenges and potential workarounds when data volumes exceed millions, if not billions, of cases.
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