Abstract
Advertising recommendation is crucial for many Internet companies because it largely affects their business income, and click-through rate (CTR) plays a key role in it. Most of the current CTR prediction models pay less attention to the feature importance before feature interaction. Besides, during bilinear feature interaction (BI), these models simply use hadamard product or inner product and implicitly introduce unnecessary feature order noise. In this paper, we propose a model called Feature Aware and Bilinear Feature Equal Interaction Network (FaBeNET). On the one hand, it can be aware of the feature importance and keep original feature as many as possible through the Squeeze-and-Excitation Residual Network (SE-ResNet); On the other hand, it assigns an interaction matrix to each feature, so the BI can be equally and effectively learned by the combination of hadamard product and inner product. On this basis, a deep neural network is used to learn higher-order feature interaction. Experiments show that FaBeNet achieves performance 0.7919 AUC and 0.4581 Logloss, which is better than other models, such as the DCN, xDeeepFM, and FiBiNET.
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Acknowledgment
This work was supported by the National High Technology Research, Development Program of China (No. 2018YFB1703500), the Shanghai Innovation Action Project of Science and Technology (No. 19511105502), and the Fundamental Research Funds for the Central Universities.
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Luo, L., Chen, Y., Liu, X., Deng, Q. (2020). Feature Aware and Bilinear Feature Equal Interaction Network for Click-Through Rate Prediction. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_36
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