An Incremental Learning framework for Large-scale CTR Prediction-Reference-Cited by-同舟云学术

An Incremental Learning framework for Large-scale CTR Prediction

Published:2022-09-18 Issue: Volume: Page:
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Container-title:Sixteenth ACM Conference on Recommender Systems
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Author:

Katsileros Petros¹,Mandilaras Nikiforos¹,Mallis Dimitrios¹,Pitsikalis Vassilis¹,Theodorakis Stavros¹,Chamiel Gil²

Affiliation:

1. Deeplab, Greece and Dept. of Algorithms and Data Science, Taboola.com, Israel

2. Dept. of Algorithms and Data Science, Taboola.com, Israel

Publisher

ACM

Reference9 articles.

1. Jordan T. Ash and Ryan P. Adams. 2020. On Warm-Starting Neural Network Training . In Proceedings of the 34th International Conference on Neural Information Processing Systems ( Vancouver, BC, Canada) (NIPS’20). Curran Associates Inc., Red Hook, NY, USA, Article 327, 11 pages. Jordan T. Ash and Ryan P. Adams. 2020. On Warm-Starting Neural Network Training. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS’20). Curran Associates Inc., Red Hook, NY, USA, Article 327, 11 pages.

2. Wide & Deep Learning for Recommender Systems

3. Ian J. Goodfellow Mehdi Mirza Da Xiao Aaron Courville and Yoshua Bengio. 2013. An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks. https://doi.org/10.48550/ARXIV.1312.6211 Ian J. Goodfellow Mehdi Mirza Da Xiao Aaron Courville and Yoshua Bengio. 2013. An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks. https://doi.org/10.48550/ARXIV.1312.6211

4. Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. https://doi.org/10.48550/ARXIV.1703.04247 Huifeng Guo Ruiming Tang Yunming Ye Zhenguo Li and Xiuqiang He. 2017. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. https://doi.org/10.48550/ARXIV.1703.04247

5. Practical Lessons from Predicting Clicks on Ads at Facebook

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