A Novel Ensemble Approach for Click-Through Rate Prediction Based on Factorization Machines and Gradient Boosting Decision Trees

Wang, Xiaochen; Hu, Gang; Lin, Haoyang; Sun, Jiayu

doi:10.1007/978-3-030-26075-0_12

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…Livne, et al [21] suggested an ensemble method using DNN and the gradient boosting decision tree (GBDT) algorithm, and several other studies have suggested methods that use neural network-based FM combined with GBDT [32,34].…”

Section: Boostingmentioning

confidence: 99%

Iterative Boosting Deep Neural Networks for Predicting Click-Through Rate

Livne¹,

Dor²,

Mazuz³

et al. 2020

Preprint

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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.

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Section: Boostingmentioning

confidence: 99%