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Article

sDeepFM: Multi-Scale Stacking Feature Interactions for Click-Through Rate Prediction

1
Guangxi Cloud Computing and Big Data Collaborative Innovation Center, Guilin 541004, China
2
Guangxi Key Laboratory of Image Graphics and Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(2), 350; https://doi.org/10.3390/electronics9020350
Received: 20 January 2020 / Revised: 13 February 2020 / Accepted: 17 February 2020 / Published: 19 February 2020
(This article belongs to the Special Issue Regularization Techniques for Machine Learning and Their Applications)
For estimating the click-through rate of advertisements, there are some problems in that the features cannot be automatically constructed, or the features built are relatively simple, or the high-order combination features are difficult to learn under sparse data. To solve these problems, we propose a novel structure multi-scale stacking pooling (MSSP) to construct multi-scale features based on different receptive fields. The structure stacks multi-scale features bi-directionally from the angles of depth and width by constructing multiple observers with different angles and different fields of view, ensuring the diversity of extracted features. Furthermore, by learning the parameters through factorization, the structure can ensure high-order features being effectively learned in sparse data. We further combine the MSSP with the classical deep neural network (DNN) to form a unified model named sDeepFM. Experimental results on two real-world datasets show that the sDeepFM outperforms state-of-the-art models with respect to area under the curve (AUC) and log loss. View Full-Text
Keywords: neural networks; deep learning; features construction; recommendation; click-through prediction neural networks; deep learning; features construction; recommendation; click-through prediction
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MDPI and ACS Style

Qiang, B.; Lu, Y.; Yang, M.; Chen, X.; Chen, J.; Cao, Y. sDeepFM: Multi-Scale Stacking Feature Interactions for Click-Through Rate Prediction. Electronics 2020, 9, 350. https://doi.org/10.3390/electronics9020350

AMA Style

Qiang B, Lu Y, Yang M, Chen X, Chen J, Cao Y. sDeepFM: Multi-Scale Stacking Feature Interactions for Click-Through Rate Prediction. Electronics. 2020; 9(2):350. https://doi.org/10.3390/electronics9020350

Chicago/Turabian Style

Qiang, Baohua; Lu, Yongquan; Yang, Minghao; Chen, Xianjun; Chen, Jinlong; Cao, Yawei. 2020. "sDeepFM: Multi-Scale Stacking Feature Interactions for Click-Through Rate Prediction" Electronics 9, no. 2: 350. https://doi.org/10.3390/electronics9020350

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