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arXiv · 2306.10050

Interpolating Item and User Fairness in Multi-Sided Recommendations

Qinyi Chen·Jason Cheuk Nam Liang·Negin Golrezaei·Djallel Bouneffouf
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Abstract

Today's online platforms heavily lean on algorithmic recommendations for bolstering user engagement and driving revenue. However, these recommendations can impact multiple stakeholders simultaneously -- the platform, items (sellers), and users (customers) -- each with their unique objectives, making it difficult to find the right middle ground that accommodates all stakeholders. To address this, we introduce a novel fair recommendation framework, Problem (FAIR), that flexibly balances multi-stakeholder interests via a constrained optimization formulation. We next explore Problem (FAIR) in a dynamic online setting where data uncertainty further adds complexity, and propose a low-regret algorithm FORM that concurrently performs real-time learning and fair recommendations, two tasks that are often at odds. Via both theoretical analysis and a numerical case study on real-world data, we demonstrate the efficacy of our framework and method in maintaining platform revenue while ensuring desired levels of fairness for both items and users.

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Qinyi Chen, Jason Cheuk Nam Liang, Negin Golrezaei, Djallel Bouneffouf. 2023-06-12. Interpolating Item and User Fairness in Multi-Sided Recommendations. https://arxiv.org/abs/2306.10050

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