Abstract: Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, contributing to phenomena such as filter bubbles and polarization. An analysis of information availability through the lens of user recourse includes a computationally efficient audit for top-N matrix factorization recommender models and may be used for adapting recommender modules to meet targets for model performance parameters within defined contexts.