FairSort: Learning to Fair Rank for Personalized Recommendations in Two-Sided Platforms

Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favorite items. This user-centric approach may lead to unfair exposure distribution among the providers. On the contrary, a provider-centric design might become unfair to the users. Therefore, this paper proposes a re-ranking model FairSort¹ to find a trade-off solution among user-side fairness, provider-side fairness, and personalized recommendations utility. Previous works habitually treat this issue as a knapsack problem, incorporating both-side fairness as constraints. In this paper, we adopt a novel perspective, treating each recommendation list as a runway rather than a knapsack. In this perspective, each item on the runway gains a velocity and runs within a specific time, achieving re-ranking for both-side fairness. Meanwhile, we ensure the Minimum Utility Guarantee for personalized recommendations by designing a Binary Search approach. This can provide more reliable recommendations compared to the conventional greedy strategy based on the knapsack problem. We further broaden the applicability of FairSort, designing two versions for online and offline recommendation scenarios. Theoretical analysis and extensive experiments on real-world datasets indicate that FairSort can ensure more reliable personalized recommendations while considering fairness for both the provider and user.

I. Introduction

Although bringing great convenience for users by mining their interests to provide personalized recommendations, the recommender system also introduces certain fairness problems. This is because recommender systems typically have inherent biases, such as data and algorithmic bias [1], [2], [3], [4], [5], resulting in unfairness in both the recommendation process and its outcomes. During the process, models may embed biased information (e.g., sensitive attributes like race, and gender), resulting in discriminatory practices like offering more technical job opportunities to men over women [6], [7]. Regarding outcomes, models may generate biased recommendations for different user groups like the unfair allocation of exposure opportunities among various providers [8], [9].