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.

针对内容提供者，我们提出了一个适用于推荐系统的追索框架，旨在理解模型在做出特定预测和推荐时的基本原理，并通过对三个真实数据集上训练的推荐系统进行实证评估，展示了 RecRec 在生成有效、稀疏和可执行的追索方面的高效性。据我们所知，这项工作是首次对推荐系统生成追索的概念进行了概括和实证测试。