ResFusion: A Residual Learning Based Fusion Framework for CTR Prediction-Reference-Cited by-同舟云学术

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ResFusion: A Residual Learning Based Fusion Framework for CTR Prediction

Published:2020 Issue: Volume: Page:29-41
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
Short-container-title:

Author:

Bao Junmei,Ji Yangguang,Yang Yonghui,Wu Le,Fu Ruiji

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-56725-5_3

Reference25 articles.

1. Abedalla, A., et al.: MTRecS-DLT: multi-modal transport recommender system using deep learning and tree models. In: 2019 SNAMS, pp. 274–278. IEEE (2019). https://doi.org/10.1109/SNAMS.2019.8931864

2. Chen, L., Wu, L., Hong, R., Zhang, K., Wang, M.: Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: AAAI2020, vol. 34, pp. 27–34 (2020)

3. Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: KDD2016, KDD 2016, p. 785–794. Association for Computing Machinery, New York (2016). https://doi.org/10.1145/2939672.2939785

4. Cheng, H., Cantú-Paz, E.: Personalized click prediction in sponsored search. In: WSDM (2010). https://doi.org/10.1145/1718487.1718531

5. Cheng, H.T., et al.: Wide & deep learning for recommender systems. https://doi.org/10.1145/2988450.2988454

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Residual Fusion Models with Neural Networks for CTR Prediction;2023 8th International Conference on Computer Science and Engineering (UBMK);2023-09-13

2. Calibrating Neural Networks for CTR Prediction;2023 31st Signal Processing and Communications Applications Conference (SIU);2023-07-05

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