Efficiently Sampling in Neural Network Training for Click-Through Rate Prediction

Finding efficient downsampling techniques has become more crucial as the training datasets for advertisement click-through rate (CTR) prediction models are growing to billions in size. We present efficient downsampling to sample CTR datasets with goals of faster training and limited decrease in the performance. We present encouraging results demonstrating the effectiveness of our approach on two publicly available CTR prediction datasets and compare efficient downsampling with stratified random downsampling.