EvalRS: a Rounded Evaluation of Recommender Systems

TL;DR

EvalRS introduces a new challenge to evaluate recommender systems in real-world scenarios, emphasizing practical behavior and robustness beyond accuracy, to foster better methodologies for real-world testing.

Contribution

It proposes EvalRS as a challenge to evaluate RSs in real-world settings, encouraging development of testing methodologies that address practical behavior and biases.

Findings

Highlights the gap between accuracy and real-world performance

Encourages development of open methodologies for in-the-wild testing

Aims to improve RS robustness and user experience

Abstract

Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces. However, research focused almost exclusively on the ability of RSs to produce accurate item rankings while giving little attention to the evaluation of RS behavior in real-world scenarios. Such narrow focus has limited the capacity of RSs to have a lasting impact in the real world and makes them vulnerable to undesired behavior, such as reinforcing data biases. We propose EvalRS as a new type of challenge, in order to foster this discussion among practitioners and build in the open new methodologies for testing RSs "in the wild".