BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction-Reference-Cited by-同舟云学术

BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Dong¹^ORCID,Salamatian Kavé²^ORCID,Xia Yunqing¹^ORCID,Deng Weiwei¹^ORCID,Zhang Qi¹^ORCID

Affiliation:

1. STCA, Microsoft Corporation, Beijing, China

2. University of Savoie, Annecy, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599780

Reference34 articles.

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3. Wide & Deep Learning for Recommender Systems

4. Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2018 . Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

5. MOBIUS

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