论文标题
推荐系统中的消费者公平性：将定义和缓解的上下文化
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
论文作者
论文摘要
通过引入消费者的公平性，为推荐系统的最终用户提供非歧视是一个关键问题，在学术界和行业中都广泛研究。当前的研究导致了各种概念，指标和不公平的缓解程序。对每个程序的评估都是异质的，并且仅限于与不公平的模型进行比较。因此，很难将每个缓解过程的影响与W.R.T.的影响相关。其他。在本文中，我们对缓解程序进行系统分析，以针对评级预测和顶级建议任务的消费者不公平。为此，我们收集了最近的顶级会议和期刊中提出的15种程序。其中只有8个可以复制。根据两个公共数据集的共同评估协议，我们随后研究了这些程序的建议实用程序和消费者公平性的影响，这是基于公平和独立性的两个主要公平概念之间的相互作用以及受不同影响损害的人口群体。我们的研究最终突出了该领域的开放挑战和未来的方向。源代码可从https://github.com/jackmedda/c-fairness-recsys获得。
Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem, widely studied in both academia and industry. Current research has led to a variety of notions, metrics, and unfairness mitigation procedures. The evaluation of each procedure has been heterogeneous and limited to a mere comparison with models not accounting for fairness. It is hence hard to contextualize the impact of each mitigation procedure w.r.t. the others. In this paper, we conduct a systematic analysis of mitigation procedures against consumer unfairness in rating prediction and top-n recommendation tasks. To this end, we collected 15 procedures proposed in recent top-tier conferences and journals. Only 8 of them could be reproduced. Under a common evaluation protocol, based on two public data sets, we then studied the extent to which recommendation utility and consumer fairness are impacted by these procedures, the interplay between two primary fairness notions based on equity and independence, and the demographic groups harmed by the disparate impact. Our study finally highlights open challenges and future directions in this field. The source code is available at https://github.com/jackmedda/C-Fairness-RecSys.
