PeNet: A feature excitation learning approach to advertisement click-through rate prediction
Author:
Funder
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Nanning Normal University
Publisher
Elsevier BV
Reference33 articles.
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3. Deng, W., Pan, J., Zhou, T., et al. (2021). DeepLight: deep lightweight feature interactions for accelerating CTR predictions in ad serving. In Proceedings of the 14th ACM international conference on web search and data mining (pp. 922–930).
4. Feng, Y., Zhu, S., & Ou, Y. (2022). Accelerating DIN Model for Online CTR Prediction with Data Compression. In 7th international conference on big data analytics (pp. 84–89).
5. Guo, W., Su, Y., Tan, R. H., et al. (2021). Dual Graph enhanced Embedding Neural Network for CTR Prediction. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining.
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