103 citations · 201 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
Offline Comparison of Ranking Functions using Randomized Data
Aman Agarwal, Xuanhui Wang, Cheng Li +2
Ranking functions return ranked lists of items, and users often interact with these items. How to evaluate ranking functions using historical interaction logs, also known as off-po…
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…