  The click-through rate (CTR) reflects the ratio of clicks on a specific item
to its total number of views. It has significant impact on websites'
advertising revenue. Learning sophisticated models to understand and predict
user behavior is essential for maximizing the CTR in recommendation systems.
Recent works have suggested new methods that replace the expensive and
time-consuming feature engineering process with a variety of deep learning (DL)
classifiers capable of capturing complicated patterns from raw data; these
methods have shown significant improvement on the CTR prediction task. While DL
techniques can learn intricate user behavior patterns, it relies on a vast
amount of data and does not perform as well when there is a limited amount of
data. We propose XDBoost, a new DL method for capturing complex patterns that
requires just a limited amount of raw data. XDBoost is an iterative three-stage
neural network model influenced by the traditional machine learning boosting
mechanism. XDBoost's components operate sequentially similar to boosting;
However, unlike conventional boosting, XDBoost does not sum the predictions
generated by its components. Instead, it utilizes these predictions as new
artificial features and enhances CTR prediction by retraining the model using
these features. Comprehensive experiments conducted to illustrate the
effectiveness of XDBoost on two datasets demonstrated its ability to outperform
existing state-of-the-art (SOTA) models for CTR prediction.
