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)
Incoming Non-self Citations Over Time
Authors
- 1. Zhiqiang Xu
- 2. Dong Li
- 3. Weijie Zhao
- 4. Xing Shen
- 5. Tianbo Huang
- 6. Xiaoyun Li
- 7. Ping Li
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,052 | HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training | 2022 | SIGMOD | 5.7337977e-05 |
| 9,402 | CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models | 2024 | SIGMOD | 4.3441378e-05 |
| 9,408 | Experimental Analysis of Large-scale Learnable Vector Storage Compression | 2024 | VLDB | 4.3441378e-05 |
| 9,806 | The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format | 2024 | SIGMOD | 4.2805224e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,319 | Sketching Linear Classifiers over Data Streams | 2018 | SIGMOD | 7.226439e-05 |
| 3,808 | SketchML: Accelerating Distributed Machine Learning with Data Sketches | 2018 | SIGMOD | 6.7455428e-05 |
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