Open Access

ARTICLE

A Recommendation System Based on Fusing Boosting Model and DNN Model

Aziguli Wulam^1,2, Yingshuai Wang^1,2, Dezheng Zhang^1,2,*, Jingyue Sang³, Alan Yang⁴

1 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
2 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China.
3 Industrial Engineering, Shanghai University, Shanghai, 200444, China.
4 Amphenol Assemble Tech, Houston, TX 77070, US.

* Corresponding Author: Dezheng Zhang. Email: .

Computers, Materials & Continua 2019, 60(3), 1003-1013. https://doi.org/10.32604/cmc.2019.07704

Abstract

In recent years, the models combining traditional machine learning with the deep learning are applied in many commodity recommendation practices. It has been proved better performance by the means of the neural network. Feature engineering has been the key to the success of many click rate estimation model. As we know, neural networks are able to extract high-order features automatically, and traditional linear models are able to extract low-order features. However, they are not necessarily efficient in learning all types of features. In traditional machine learning, gradient boosting decision tree is a typical representative of the tree model, which can construct new features related before and after tree. Convolutional neural networks have a better perception of local features. In this paper, we take advantage of convolutional networks to capture the local features. The features are constructed by the node leaf of gradient boosting decision tree. This paper employs the tree leaf node to mine the user behavior path features, and uses the deep model to extract the user abstract features. Based on a Kaggle competition, our model performs better in the test data than any other model.

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Citations