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

摘要: Traditional recommendation systems focus on max#2;imizing user satisfaction by suggesting their favorite items. Thisuser-centric approach may lead to unfair exposure distributionamong the providers. On the contrary, a provider-centric designmight become unfair to the users. Therefore, this paper pro#2;poses a re-ranking model FairSort1to find a trade-off solutionamong user-side fairness, provider-side fairness, and personalizedrecommendations utility. Previous works habitually treat thisissue as a knapsack problem, incorporating both-side fairnessas constraints.In this paper, we adopt a novel perspective, treating eachrecommendation list as a runway rather than a knapsack. Inthis perspective, each item on the runway gains a velocity andruns within a specific time, achieving re-ranking for both-sidefairness. Meanwhile, we ensure the Minimum Utility Guaranteefor personalized recommendations by designing a Binary Searchapproach. This can provide more reliable recommendations com#2;pared to the conventional greedy strategy based on the knapsackproblem. We further broaden the applicability of FairSort,designing two versions for online and offline recommendationscenarios. Theoretical analysis and extensive experiments on real#2;world datasets indicate that FairSort can ensure more reliablepersonalized recommendations while considering fairness forboth the provider and user.