Aziguli Wulam1,2, Yingshuai Wang1,2, Dezheng Zhang1,2,*, Jingyue Sang3, Alan Yang4
CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1003-1013, 2019, DOI: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… More >
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