4. Fairness of Exposure in Rankings. Singh, Ashudeep,Joachims, Thorsten. 2018 推荐系统不仅要对用户负责,也要对被推荐的物品负责。本文从曝光分配公平性的角度入手,通过一系列定义和推导将问题转化为线性规划,建立了一个在公平性限制下ranking问题的分析和求解框架。 5. Equity of Attention:Amortizing Individual...
4. Fairness of Exposure in Rankings. Singh, Ashudeep,Joachims, Thorsten. 2018 推荐系统不仅要对用户负责,也要对被推荐的物品负责。本文从曝光分配公平性的角度入手,通过一系列定义和推导将问题转化为线性规划,建立了一个在公平性限制下ranking问题的分析和求解框架。 5. Equity of Attention:Amortizing Individual...
为了高效地实现这一目标,作者借鉴已有的SLIM算法并进行了改进。 4. Fairness of Exposure in Rankings. Singh, Ashudeep,Joachims, Thorsten. 2018 推荐系统不仅要对用户负责,也要对被推荐的物品负责。本文从曝光分配公平性的角度入手,通过一系列定义和推导将问题转化为线性规划,建立了一个在公平性限制下ranking问题的...
In this paper, we propose the first efficient online algorithm to optimize concave objective functions in the space of rankings which applies to every concave and smooth objective function, such as the ones found for fairness of exposure. Based on online variants of the Frank-Wolfe algorithm, we...
In this article, we pay special attention to the concept of fairness in rankings and recommendations. By fairness, we typically mean lack of discrimination (bias). Bias may come from the algorithm, reflecting, for example, commercial or other preferences of its designers, or even from the actua...
In: Proc. of the Conference on Fairness, Accountability, and Transparency, FAT* 2019, pp. 59–68 (2019). https://doi.org/10.1145/3287560.3287598 Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery...
Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a growing understanding that the latter is important to consider for a wide range of ranking applications (e.g....
Operationalizing the Legal Principle of Data Minimization for Personalization Asia J. Biega, Peter Potash, Hal Daumé III, Fernando Diaz, Michèle Finck SIGIR 2020 | July 2020 PDF Evaluating Stochastic Rankings with Expected Exposure Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J...
(Jin et al., 2023, Wu et al., 2022). From the perspective of providers, the unfair treatment they suffer in the recommendation system is mainly that the items they provide cannot get more exposure in the recommendation system, while a small number of items occupy the front row of the ...
内容提示: Maximizing Marginal Fairness for Dynamic Learning to RankTao YangUniversity of UtahSalt Lake City, Utahtaoyang@cs.utah.eduQingyao AiUniversity of UtahSalt Lake City, Utahaiqy@cs.utah.eduABSTRACTRankings, especially those in search and recommendation systems,often determine how people access...