一句话：提出 FairRec 统一框架与双端离散粒子群搜索测试方法，从多视角检测深度推荐系统的隐藏公平性问题。
深度推荐系统广泛部署，但可能对特定用户或物品存在不公平，如推荐质量差异或曝光机会不均。现有针对传统深度分类模型的公平性概念与测试方法难以直接用于推荐系统，且缺乏系统映射。本文提出 FairRec，一个支持从模型效用、物品多样性、物品流行度等多角度定制化公平性测试的统一框架，并设计双端离散粒子群优化（DPSO）搜索算法，在大量候选群体中高效搜索以不利群体形式存在的隐藏公平性问题。基于测试报告，对识别出的不利群体采用简单重排序缓解策略可显著提升公平性。在多个企业级推荐系统上的实验表明，FairRec 能以约一半至八分之一的时间达到约 95% 的测试准确率，有效且高效地发现深层公平性问题。
Deep learning-based recommender systems (DRSs) are increasingly and widely deployed in the industry, which brings significant convenience to people’s daily life in different ways. However, recommender systems are also shown to suffer from multiple issues, e.g., the echo chamber and the Matthew effect, of which the notation of “fairness” plays a core role. For instance, the system may be regarded as unfair to 1) a specific user, if the user gets worse recommendations than other users, or 2) an item (to recommend), if the item is much less likely to be exposed to the users than other items. While many fairness notations and corresponding fairness testing approaches have been developed for traditional deep classification models, they are essentially hardly applicable to DRSs. One major challenge is that there still lacks a systematic understanding and mapping between the existing fairness notations and the diverse testing requirements for deep recommender systems, not to mention further testing or debugging activities. To address the gap, we propose FairRec, a unified framework that supports fairness testing of DRSs from multiple customized perspectives, e.g., model utility, item diversity, item popularity, etc. We also propose a novel, efficient search-based testing approach to tackle the new challenge, i.e., double-ended discrete particle swarm optimization (DPSO) algorithm, to effectively search for hidden fairness issues in the form of certain disadvantaged groups from a vast number of candidate groups. Given the testing report, by adopting a simple re-ranking mitigation strategy on these identified disadvantaged groups, we show that the fairness of DRSs can be significantly improved. We conducted extensive experiments on multiple industry-level DRSs adopted by leading companies. The results confirm that FairRec is effective and efficient in identifying the deeply hidden fairness issues, e.g., achieving ∼95% testing accuracy with ∼half to 1/8 time.
@inproceedings{paperbot8044,
title = {FairRec: Fairness Testing for Deep Recommender Systems},
author = {Huizhong Guo and Jinfeng Li and Jingyi Wang and Xiangyu Liu and Dongxia Wang and Zehong Hu and Rong Zhang and Hui Xue},
booktitle = {ISSTA},
year = {2023},
doi = {10.1145/3597926.3598058}
}