8 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.IR2025★ 1 cited
Addressing Personalized Bias for Unbiased Learning to Rank
Zechun Niu, Lang Mei, Liu Yang +4
Unbiased learning to rank (ULTR), which aims to learn unbiased ranking models from biased user behavior logs, plays an important role in Web search. Previous research on ULTR has s…
cs.LG2024★ 1 cited
Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study
Zechun Niu, Zhilin Zhang, Jiaxin Mao +2
Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community for its ability to leverage massive logged user interaction data to train ranking model…
cs.IR2020★ 8 cited
Unbiased Learning to Rank: Online or Offline?
Qingyao Ai, Tao Yang, Huazheng Wang +1
How to obtain an unbiased ranking model by learning to rank with biased user feedback is an important research question for IR. Existing work on unbiased learning to rank (ULTR) ca…