most citedControlling Fairness and Bias in Dynamic Learning-to-Rank

208 citations · 250 across the 2 of their papers we have counts for

collaborators

2 papers

cs.IR2020★ 208 cited

Controlling Fairness and Bias in Dynamic Learning-to-Rank

Marco Morik, Ashudeep Singh, Jessica Hong +1

Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw…

cs.LG2019★ 42 cited

Policy Learning for Fairness in Ranking

Ashudeep Singh, Thorsten Joachims

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…