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Agile and Accurate CTR Prediction Model Training for Massive-Scale Online Advertising Systems

Summary: Industrial-scale CTR training on GPUs with quantization to handle hundreds of billions of features and samples. Quantization enlarges embedding capacity without extra storage, enabling agile deployment and yielding 1% revenue lift and 1.8% relative CTR gain in production. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6189
Venue
SIGMOD
Year
2021
Pagerank
5.8185371e-05
Overall Rank
6,633 | 54.50%
DOI
10.1145/3448016.3457236

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{xu_sigmod21,
        title = {{Agile and Accurate CTR Prediction Model Training for Massive-Scale Online Advertising Systems}},
        author = {Xu, Zhiqiang and Li, Dong and Zhao, Weijie and Shen, Xing and Huang, Tianbo and Li, Xiaoyun and Li, Ping},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457236},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457236},
        year = {2021}
}

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
3,445 Sketching Linear Classifiers over Data Streams 2018 SIGMOD 7.4089141e-05
4,083 SketchML: Accelerating Distributed Machine Learning with Data Sketches 2018 SIGMOD 6.9160949e-05
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