  Recommender systems play an essential role in the choices people make in
domains such as entertainment, shopping, food, news, employment, and education.
The machine learning models underlying these recommender systems are often
enormously large and black-box in nature for users, content providers, and
system developers alike. It is often crucial for all stakeholders to understand
the model's rationale behind making certain predictions and recommendations.
This is especially true for the content providers whose livelihoods depend on
the recommender system. Drawing motivation from the practitioners' need, in
this work, we propose a recourse framework for recommender systems, targeted
towards the content providers. Algorithmic recourse in the recommendation
setting is a set of actions that, if executed, would modify the recommendations
(or ranking) of an item in the desired manner. A recourse suggests actions of
the form: "if a feature changes X to Y, then the ranking of that item for a set
of users will change to Z." Furthermore, we demonstrate that RecRec is highly
effective in generating valid, sparse, and actionable recourses through an
empirical evaluation of recommender systems trained on three real-world
datasets. To the best of our knowledge, this work is the first to conceptualize
and empirically test a generalized framework for generating recourses for
recommender systems.
