Recommendation as Generalization: Evaluating Cognitive Models In the Wild
- David Bourgin, Computational Cognitive Science Lab, UC Berkeley, Berkeley, California, United States
- Joshua Abbott, Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
- Tom Griffiths, University of California, Berkeley, Berkeley, California, United States
AbstractThe explosion of data generated during human interactions online presents an opportunity for cognitive scientists to evaluate their models on popular real-world tasks outside the confines of the laboratory. We demonstrate this approach by evaluating two cognitive models of generalization against two machine learning approaches to recommendation on an online dataset of over 100K human playlist selections. Across two experiments we demonstrate that a model from cognitive science can both be efficiently implemented at scale and can capture generalization trends in human recommendation judgments which neither machine learning model is capable of replicating. We use these results to illustrate the opportunity internet-scale datasets offer to cognitive scientists, as well as to underscore the importance of using insights from cognitive modeling to supplement the standard predictive-analytic approach taken by many existing machine learning approaches.
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