Residual Fusion Models with Neural Networks for CTR Prediction

Ergun Biçici. Residual Fusion Models with Neural Networks for CTR Prediction. 2023 8th International Conference on Computer Science and Engineering (UBMK), Burdur, Turkiye, 2023, pp. 01-04, doi: 10.1109/UBMK59864.2023.10286706. URL: https://ieeexplore.ieee.org/document/10286706

No single prediction model achieves the best performance on all datasets and we are better off combining the strengths of different models for each task. Residual fusion learning is a two step combination method that trains a second model on the residual of the target from the first model's prediction. In the final phase, the predictions of both of these models are added. We use gradient boosting decision trees (GBDT) and neural networks for the initial model in residual fusion and compare three GBDT models and four neural network models. We introduce residual fusion with two different neural network models and show that we can achieve AUC gains that reach 0.95%.