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

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ID: 103866
2020
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Abstract
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.
Reference Key
qiang2020sdeepfmelectronics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Qiang, Baohua;Lu, Yongquan;Yang, Minghao;Chen, Xianjun;Chen, Jinlong;Cao, Yawei;
Journal Electronics
Year 2020
DOI
DOI not found
URL
https://www.mdpi.com/2079-9292/9/2/350
Keywords
chemistry Engineering (General). Civil engineering (General) Technology physics telecommunication electronics applied optics. photonics

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