This repository contains the implementation of VLDB 2025 submission paper: Mixed-Precision Embeddings for Large-Scale Recommendation Models.
Please (1) download the Avazu, Criteo, KDD12 datasets; (2) place the training data as follows; (3) run the corresponding shells to preprocess the datasets; (4) run 'python ./dataloader/feat_cnt.py' to count the feature frequencies.
├── dataprocess
├── avazu.sh
├── avazu_new
├── train.csv
├── criteo.sh
├── criteo_new
├── train.txt
├── kdd12.sh
├── kdd12_new
├── training.txt
The 'run' folder contains scripts for testing all methods. Note that the corresponding hyperparameters in the scripts are the optimal configurations.
./run/12-avazu-optfp.sh;
./run/22-criteo-optfp.sh;
./run/32-kdd12-optfp.sh;
Avazu | Criteo | KDD12 | |
---|---|---|---|
Backbone | lr=1e-3, l2=0.0 | lr=1e-3, l2=3e-6 | lr=1e-3, l2=0.0 |
QR-Trick | qr_ratio=2 | qr_ratio=2 | qr_ratio=2 |
PEP | pep_init=-11 | pep_init=-11 | pep_init=-11 |
OptFS | tau=2e-2, optfs_l1=1.75e-10 | tau=1e-3, optfs_l1=1e-8 | tau=1e-2, optfs_l1=1e-9 |
ALPT | bit=8, lr_alpha=1e-6 | bit=8, lr_alpha=1e-6 | bit=8, lr_alpha=1e-6 |
LSQ+ | bit=6, lr_alpha=1e-3 | bit=6, lr_alpha=1e-3 | bit=6, lr_alpha=1e-3 |
MPE |
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