Journal
ELECTRONICS
Volume 9, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/electronics9020350
Keywords
neural networks; deep learning; features construction; recommendation; click-through prediction
Categories
Funding
- National Natural Science Foundation of China [61762025]
- Guangxi Key Research and Development Program [AB17195053, AB18126053]
- Natural Science Foundation of Guangxi of China [2017GXNSFAA198226, 2019GXNSFDA185007]
- Guilin Science and Technology Development Program [20180107-4]
- Innovation Project of GUET Graduate Education [2019YCXS051]
- Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Image and Graphics [GIIP201603]
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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.
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