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

TL;DR

FairSort introduces a novel re-ranking approach for personalized recommendations that balances user satisfaction and provider fairness, using a runway analogy and binary search to improve reliability and fairness.

Contribution

This work presents a new perspective treating recommendation lists as runways, enabling better fairness and utility trade-offs compared to traditional knapsack-based methods.

Findings

FairSort achieves improved fairness for providers and users.

The binary search approach enhances recommendation reliability.

Extensive experiments validate the effectiveness of FairSort.

Abstract

Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favourite 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 FairSort 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.