Abstract
Click-Through Rate (CTR) prediction has become an important part of many enterprise applications, such as recommendation systems and online advertising. In recent years, some models based on deep learning have been applied to the CTR prediction systems. Although the accuracy is improving, the complexity of the model is constantly increasing. In this paper, we propose a novel model called Domain-based Feature Interactions Learning via Attention Networks (DFILAN), which can effectively reduce model complexity and automatically learn the importance of feature interactions. On the one hand, the DFILAN divides the input features into several domains to reduce the time complexity of the model in the interaction process. On the other hand, the DFILAN interacts at the embedding vector dimension level to improve the feature interactions effect and leverages the attention network to automatically learn the importance of feature interactions. Extensive experiments conducted on the two public datasets show that DFILAN is effective and outperforms the state-of-the-art models.
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Acknowledgements
This work is supported by Tianjin “Project + Team” Key Training Project (XC202022), the National Nature Science Foundation of China (61702368), and the Natural Science Foundation of Tianjin (18JCQNJC00700).
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Han, Y., Xiao, Y., Wang, H., Zheng, W., Zhu, K. (2021). DFILAN: Domain-Based Feature Interactions Learning via Attention Networks for CTR Prediction. In: Jensen, C.S., et al. Database Systems for Advanced Applications. DASFAA 2021. Lecture Notes in Computer Science(), vol 12682. Springer, Cham. https://doi.org/10.1007/978-3-030-73197-7_33
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DOI: https://doi.org/10.1007/978-3-030-73197-7_33
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