Dynamic Explicit Embedding Representation for Numerical Features in Deep CTR Prediction

Author:

Cheng Yuan1

Affiliation:

1. Career Science Lab, BOSS Zhipin, Beijing, China

Publisher

ACM

Reference24 articles.

1. Wide & Deep Learning for Recommender Systems

2. Looking at CTR Prediction Again: Is Attention All You Need?

3. Deep Neural Networks for YouTube Recommendations

4. Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Volume 1 (Long and Short Papers). Association for Computational Linguistics, 4171--4186. https://doi.org/10. 18653/v1/N19--1423 10.18653/v1 Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 4171--4186. https://doi.org/10.18653/v1/N19--1423

5. Xavier Glorot and Yoshua Bengio . 2010 . Understanding the difficulty of training deep feedforward neural networks . In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research , Vol. 9),, Yee Whye Teh and Mike Titterington (Eds.). PMLR, Chia Laguna Resort, Sardinia, Italy, 249-- 256 . http://proceedings.mlr.press/v9/glorot10a.html Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 9),, Yee Whye Teh and Mike Titterington (Eds.). PMLR, Chia Laguna Resort, Sardinia, Italy, 249--256. http://proceedings.mlr.press/v9/glorot10a.html

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1. Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples;Companion Proceedings of the ACM Web Conference 2024;2024-05-13

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

3. Feature Representation Enhancing by Context Sensitive Information in CTR Prediction;Advanced Data Mining and Applications;2023

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