Counterfactual Learning to Rank using Heterogeneous Treatment Effect Estimation

19 Jul 2020Mucun TianChun GuoVito OstuniZhen Zhu

Learning-to-Rank (LTR) models trained from implicit feedback (e.g. clicks) suffer from inherent biases. A well-known one is the position bias -- documents in top positions are more likely to receive clicks due in part to their position advantages... (read more)

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