Residual Connections Improve Prediction Performance

Click-through rate (CTR) prediction is a critical task in online advertising and recommendation systems, where the depth and complexity of learning models have become increasingly challenging. This paper addresses the challenges associated with CTR prediction by introducing residual connections to enhance CTR prediction models. In this paper, we investigate the integration of residual connections into CTR prediction models. Experiments involve the application of plain MaskNet and MaskNet enhanced with residual connections on benchmark datasets from both the company and Avazu. Our findings demonstrate that residual connections can be effectively integrated into CTR prediction models, increasing the AUC by 0.77% and decreasing the loss by 2.8%.