RecRec: Algorithmic Recourse for Recommender Systems

Abstract

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.

Accepted as a short paper at CIKM 2023

References in corpus (6)

• Towards A Rigorous Science of Interpretable Machine Learning
• Equality of Opportunity in Supervised Learning
• Theoretically Principled Trade-off between Robustness and Accuracy
• Improving fairness in machine learning systems: What do industry practitioners need?
• Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
• Quantifying Interpretability and Trust in Machine Learning Systems