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- W3088393583 abstract "Click-through rate (CTR) prediction is a core problem of building advertising systems. In many real-world applications, because an ad placed in various positions has different click probabilities, the position information should be considered in both training and prediction. For such position-aware systems, existing approaches learn CTR models from clicks/not-clicks on historically displayed events by leveraging the position information in different ways. In this work, we explain that these approaches may give a heavily biased model. We first point out that in position-aware systems, two different types of selection biases coexist in displayed events. Secondly, we explain that some approaches attempting to eliminate the position effect from clicks/not-clicks may possess an additional bias. Finally, to obtain an unbiased CTR model for position-aware systems, we propose a novel counterfactual learning framework. Experiments confirm both our analysis on selection biases and the effectiveness of our proposed counterfactual learning framework." @default.
- W3088393583 created "2020-10-01" @default.
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- W3088393583 date "2020-09-22" @default.
- W3088393583 modified "2023-10-18" @default.
- W3088393583 title "Unbiased Ad Click Prediction for Position-aware Advertising Systems" @default.
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- W3088393583 doi "https://doi.org/10.1145/3383313.3412241" @default.
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