CGM

Gradient Matching for Categorical Data Distillation in CTR Prediction

Cheng Wang, Jiacheng Sun, Zhenhua Dong, Ruixuan Li, Rui Zhang

RecSys 2023 · first public 2023-01

paper ↗catalogued✓ abstract read

In one paragraph

Proposes CGM (Categorical data distillation with Gradient Matching), which extends gradient-matching dataset distillation to the high-dimensional, sparse categorical features of click-through-rate prediction data, addressing the blocked gradient flow through categorical embeddings and the cost of the resulting bi-level optimization; distills a small synthetic dataset that trains CTR models from scratch toward performance close to training on the full data.

Where it sits

Gradient matching (Surrogate matching)
Setting: Other data types

BibTeX (generated; prefer the venue's official entry)

@article{wang2023gradient,
  title   = {Gradient Matching for Categorical Data Distillation in CTR Prediction},
  author  = {Cheng Wang and Jiacheng Sun and Zhenhua Dong and Ruixuan Li and Rui Zhang},
  journal = {RecSys 2023},
  year    = {2023}
}

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