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Found 2 papers, 2 papers with code
Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation
To unbiasedly learn to rank, existing counterfactual frameworks first estimate the propensity (probability) of missing clicks with intervention data from a small portion of search traffic, and then use inverse propensity score (IPS) to debias LTR algorithms on the whole data set.
Estimating Error and Bias in Offline Evaluation Results
Offline evaluations of recommender systems attempt to estimate users' satisfaction with recommendations using static data from prior user interactions.
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