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

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

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...

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Bibliographic Details
Main Authors: Baohua Qiang, Yongquan Lu, Minghao Yang, Xianjun Chen, Jinlong Chen, Yawei Cao
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Electronics
Subjects:
neural networks
deep learning
features construction
recommendation
click-through prediction
Online Access:https://www.mdpi.com/2079-9292/9/2/350

Internet

https://www.mdpi.com/2079-9292/9/2/350

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