601 citations · 2.1k across the 56 of their papers we have counts for
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Estimating Position Bias without Intrusive Interventions
Aman Agarwal, Ivan Zaitsev, Xuanhui Wang +3
Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal. While it was recently shown how counter…
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…
Intervention Harvesting for Context-Dependent Examination-Bias Estimation
Zhichong Fang, Aman Agarwal, Thorsten Joachims
Accurate estimates of examination bias are crucial for unbiased learning-to-rank from implicit feedback in search engines and recommender systems, since they enable the use of Inve…
Consistent Position Bias Estimation without Online Interventions for Learning-to-Rank
Aman Agarwal, Ivan Zaitsev, Thorsten Joachims
Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal with uninformative signals due to positi…
A General Framework for Counterfactual Learning-to-Rank
Aman Agarwal, Kenta Takatsu, Ivan Zaitsev +1
Implicit feedback (e.g., click, dwell time) is an attractive source of training data for Learning-to-Rank, but its naive use leads to learning results that are distorted by present…
Improving Recommender Systems Beyond the Algorithm
Tobias Schnabel, Paul N. Bennett, Thorsten Joachims
Recommender systems rely heavily on the predictive accuracy of the learning algorithm. Most work on improving accuracy has focused on the learning algorithm itself. We argue that t…