Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems

TL;DR

This paper introduces a re-ranking method for recommender systems that balances accuracy and fairness by incentivizing content creators to promote long-tail items, addressing data bias and improving diversity.

Contribution

It presents a novel end-to-end optimization approach using differentiable ranking operators for fair exposure driven by strategic agents in dynamic settings.

Findings

Effective in increasing visibility of tail items

Balances recommendation accuracy with fairness

Proven on public and industrial datasets

Abstract

Data bias, e.g., popularity impairs the dynamics of two-sided markets within recommender systems. This overshadows the less visible but potentially intriguing long-tail items that could capture user interest. Despite the abundance of research surrounding this issue, it still poses challenges and remains a hot topic in academic circles. Along this line, in this paper, we developed a re-ranking approach in dynamic settings with fair-exposure optimization driven by strategic agents. Designed for the producer side, the execution of agents assumes content creators can modify item features based on strategic incentives to maximize their exposure. This iterative process entails an end-to-end optimization, employing differentiable ranking operators that simultaneously target accuracy and fairness. Joint objectives ensure the performance of recommendations while enhancing the visibility of tail items. We also leveraged the performativity nature of predictions to illustrate how strategic learning influences content creators to shift towards fairness efficiently, thereby incentivizing features of tail items. Through comprehensive experiments on both public and industrial datasets, we have substantiated the effectiveness and dominance of the proposed method especially on unveiling the potential of tail items.