In pursuit of fairness and balanced development, recommender systems (RS) often prioritize group fairness, ensuring that specific groups maintain a minimum level of exposure over a given period. For example, RS platforms aim to ensure adequate exposure for new providers or specific categories of items according to their needs. Modern industry RS usually adopts a two-stage pipeline: stage-1 (retrieval stage) retrieves hundreds of candidates from millions of items distributed across various servers, and stage-2 (ranking stage) focuses on presenting a small-size but accurate selection from items chosen in stage-1. Existing efforts for ensuring amortized group exposures focus on stage-2, however, stage-1 is also critical for the task. Without a high-quality set of candidates, the stage-2 ranker cannot ensure the required exposure of groups. Previous fairness-aware works designed for stage-2 typically require accessing and traversing all items. In stage-1, however, millions of items are distributively stored in servers, making it infeasible to traverse all of them. How to ensure group exposures in the distributed retrieval process is a challenging question. To address this issue, we introduce a model named FairSync, which transforms the problem into a constrained distributed optimization problem. Specifically, FairSync resolves the issue by moving it to the dual space, where a central node aggregates historical fairness data into a vector and distributes it to all servers. To trade off the efficiency and accuracy, the gradient descent technique is used to periodically update the parameter of the dual vector. The experiment results on two public recommender retrieval datasets showcased that FairSync outperformed all the baselines, achieving the desired minimum level of exposures while maintaining a high level of retrieval accuracy.

翻译：为追求公平性与均衡发展，推荐系统（RS）常聚焦于群体公平，确保特定群体在给定时间段内维持最低曝光量。例如，RS平台旨在根据需求为新兴内容提供方或特定类目商品保障充足曝光。现代工业级RS通常采用两阶段流水线：第一阶段（检索阶段）从分布于不同服务器的数百万候选物品中检索数百个候选结果，第二阶段（排序阶段）则致力于从第一阶段所选物品中呈现数量有限但精准的排序结果。现有保障分期群体曝光的研究主要聚焦于第二阶段，然而第一阶段对该任务同样至关重要。若缺乏高质量的候选集，第二阶段排序器将无法保证群体所需的曝光量。针对第二阶段设计的现有公平性方法通常需要访问并遍历所有物品。但在第一阶段中，数百万物品分布式存储于各服务器，遍历所有物品并不可行。如何在分布式检索过程中保障群体曝光是一个具有挑战性的问题。为此，我们提出名为FairSync的模型，将该问题转化为约束性分布式优化问题。具体而言，FairSync通过将问题迁移至对偶空间解决：中央节点将历史公平性数据聚合为向量，并分发至所有服务器。为兼顾效率与精度，采用梯度下降技术定期更新对偶向量的参数。在两个公开推荐检索数据集上的实验结果表明，FairSync性能超越所有基线方法，在保持高检索精度的同时实现了所需的最低曝光量。