Many large-scale recommender systems consist of two stages, where the first stage focuses on efficiently generating a small subset of promising candidates from a huge pool of items for the second-stage model to curate final recommendations from. In this paper, we investigate how to ensure groups fairness to the items in this two-stage paradigm. In particular, we find that existing first-stage recommenders might select an irrecoverably unfair set of candidates such that there is no hope for the second-stage recommender to deliver fair recommendations. To this end, we propose two threshold-policy selection rules that, given any relevance model of queries and items and a point-wise lower confidence bound on the expected number of relevant items for each policy, find near-optimal sets of candidates that contain enough relevant items in expectation from each group of items. To instantiate the rules, we demonstrate how to derive such confidence bounds from potentially partial and biased user feedback data, which are abundant in many large-scale recommender systems. In addition, we provide both finite-sample and asymptotic analysis of how close the two threshold selection rules are to the optimal thresholds. Beyond this theoretical analysis, we show empirically that these two rules can consistently select enough relevant items from each group while minimizing the size of the candidate sets for a wide range of settings.

本文研究如何在两阶段的推荐系统中保证物品的群体公平性，并提出了两种阈值策略选择规则，从可能存在的部分偏倚的用户反馈数据中推导出置信下限，以近似达到最优阈值的同时，充分考虑了物品在每个组中的期望相关性，实现了充分选取每个组的相关物品并最小化候选集大小。

两阶段推荐系统公平性的不确定性量化