3 papers
cs.LG2021
Fairness of Exposure in Stochastic Bandits
Lequn Wang, Yiwei Bai, Wen Sun +1
Contextual bandit algorithms have become widely used for recommendation in online systems (e.g. marketplaces, music streaming, news), where they now wield substantial influence on…
cs.IR2020
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets
Lequn Wang, Thorsten Joachims
Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a…
cs.LG2018
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
Yi Su, Lequn Wang, Michele Santacatterina +1
The ability to perform offline A/B-testing and off-policy learning using logged contextual bandit feedback is highly desirable in a broad range of applications, including recommend…