|
搜索和推荐系统中的公平性排名，并应用于LinkedIn人才搜索
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search |
|
| |
| 课程网址: |
http://videolectures.net/kdd2019_geyik_ambler_kenthapadi/
|
| 主讲教师: |
Sahin Geyik
|
| 开课单位: |
领英公司
|
| 开课时间: |
2020-03-02
|
| 课程语种: |
英语
|
| 中文简介: |
我们提出了一个框架，用于量化和缓解为个人排名设计的机制中的算法偏差，通常用作网络规模搜索和推荐系统的一部分。我们首先提出补充措施，量化性别和年龄等受保护属性方面的偏差。然后，我们提出了计算公平性感知结果重新排序的算法。对于给定的搜索或推荐任务，我们的算法寻求实现一个或多个受保护属性的排名靠前的结果的期望分布。我们表明，这种框架可以根据所需分配的选择进行调整，以实现机会平等和人口均等等公平标准。我们通过对不同参数选择的广泛模拟来评估所提出的算法，并研究公平感知排名对偏差和效用测度的影响。最后，我们展示了将我们的框架应用于领英人才搜索代表排名的在线A/B测试结果，并讨论了实践中的经验教训。我们的方法在不影响业务指标的情况下极大地提高了公平性指标（具有代表性结果的搜索查询数量增加了近三倍），为全球100%的LinkedIn Recruiter用户部署铺平了道路。我们是第一个确保招聘领域公平的大规模部署框架，对超过6.3亿LinkedIn成员具有潜在的积极影响。
|
| 课程简介: |
We present a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems. We first propose complementary measures to quantify bias with respect to protected attributes such as gender and age. We then present algorithms for computing fairness-aware re-ranking of results. For a given search or recommendation task, our algorithms seek to achieve a desired distribution of top ranked results with respect to one or more protected attributes. We show that such a framework can be tailored to achieve fairness criteria such as equality of opportunity and demographic parity depending on the choice of the desired distribution. We evaluate the proposed algorithms via extensive simulations over different parameter choices, and study the effect of fairness-aware ranking on both bias and utility measures. We finally present the online A/B testing results from applying our framework towards representative ranking in LinkedIn Talent Search, and discuss the lessons learned in practice. Our approach resulted in tremendous improvement in the fairness metrics (nearly three fold increase in the number of search queries with representative results) without affecting the business metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users worldwide. Ours is the first large-scale deployed framework for ensuring fairness in the hiring domain, with the potential positive impact for more than 630M LinkedIn members.
|
| 关 键 词: |
搜索和推荐系统; 公平性排名; LinkedIn人才搜索
|
| 课程来源: |
视频讲座网
|
| 数据采集: |
2022-09-15：cyh
|
| 最后编审: |
2022-09-19：cyh
|
| 阅读次数: |
27
|
|
图 书 馆