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In display advertising, predicting the conversion rate, that is, theprobability that a user takes a predefined action on an advertiser's website,such as purchasing goods is fundamental in estimating the value of displayingthe advertisement. However, there is a relatively long time delay between aclick and its resultant conversion. Because of the delayed feedback, somepositive instances at the training period are labeled as negative because someconversions have not yet occurred when training data are gathered. As a result,the conditional label distributions differ between the training data and theproduction environment. This situation is referred to as a feedback shift. Weaddress this problem by using an importance weight approach typically used forcovariate shift correction. We prove its consistency for the feedback shift.Results in both offline and online experiments show that our proposed methodoutperforms the existing method.

A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback