Designing Recommender Systems with Reachability in Mind
Participatory Aproaches to Machine Learning Workshop @ ICML, 2020
Sarah Dean, Mihaela Curmei, Benjamin Recht https://people.eecs.berkeley.edu/~sarahdean/stochastic_reachability.pdf
Access to digital content is often mediated by recommendations, which are primarily designed to predict user preferences. We take an alternate view in this work, exploring mechanisms which determine the availability of content from the perspective of user agency. We introduce a notion of reachability and show how it is determined by preference scoring functions, item selection rules, and user interface decisions. Simulated experiments illustrate these insights.