Abstract
Traditional CTR recommendation models have concentrated on how to learn low-order and high-order characteristics. The majority of them make many efforts at combining low-order and high-order functions. However, they ignore the importance of the attention mechanism for learning input features. The ECABiNet model is proposed in this article to enhance the performance of CTR. On the one hand, the ECABiNet model can learn the importance of features dynamically via the LayerNorm and ECANET layers. On the other hand, through the use of a bi-interaction layer and a DNN layer, it is capable of effectively learning the feature interactions. According to the experimental results on two public datasets, the ECABiNet model is more effective than the previous CTR model.
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Acknowledgment
This work is supported by Hainan Province Science and Technology Special Fund, which is Research and Application of Intelligent Recommendation Technology Based on Knowledge Graph and User Portrait (No.ZDYF2020039). Thanks to Professor CaiMao Li, the correspondent of this paper.
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Hou, Y., Li, C., Li, H., Lin, H., Chen, Q. (2022). Focusing on the Importance of Features for CTR Prediction. In: Wang, Y., Zhu, G., Han, Q., Wang, H., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2022. Communications in Computer and Information Science, vol 1628. Springer, Singapore. https://doi.org/10.1007/978-981-19-5194-7_4
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