Unifying recommendation and active learning for human-algorithm interactions


The enormous scale of the available information and products on the Internet has necessitated the development of algorithms that intermediate between options and human users. These algorithms do not select information at random, but attempt to provide the user with relevant information. In doing so, the algorithms may incur potential negative consequences related to, for example, "filter bubbles." Building from existing algorithms, we introduce a parametrized model that unifies and interpolates between recommending relevant information and active learning. In a concept learning paradigm, we illustrate the trade-offs of optimizing prediction and recommendation, show that there is a broad parameter region of stable performance that optimizes for both, identify a specific regime that is most robust to human variability, and identify the cause of this optimized performance. We conclude by discussing implications for the cognitive science of concept learning and the practice of machine learning in the real world.

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