Drop_Noise_For_CTR_Prediction.pdf (195.03 kB)
Download fileDrop Noise For CTR Prediction
Click-through rate (CTR) prediction is task to estimate the
possibility of user clicking on a recommended item. The ground truth
labels of this task are the click history of users. As there are many reasons
why noise data or low quality data may be generated, the training data
has a certain amount of noise data or low quality data. In this work,
We propose a simple but effective method to find the noise data. Our
method could improve the offline AUC from 0.60 to 0.75 on our realworld dataset.