RecRec: Algorithmic Recourse for Recommender Systems

TL;DR

This paper introduces RecRec, a novel framework for generating actionable recourses in recommender systems, helping content providers understand and influence recommendation outcomes effectively.

Contribution

It presents the first generalized framework for algorithmic recourse in recommender systems, with empirical validation on real-world datasets.

Findings

RecRec generates valid, sparse, and actionable recourses.

The framework is effective across multiple real-world datasets.

It enhances understanding and control over recommendation outcomes.

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.