OptInter

This repository contains PyTorch Implementation of ICDE 2022 paper:

Memorize, factorize, or be naive: Learning optimal feature interaction methods for CTR Prediction.

You can find our paper here.

Usage

Following the steps below to run our codes:

Install

pip install -r requirements.txt

Download Dataset

Please Download the Criteo and Avazu datasets.

• For Criteo dataset, copy the train.txt file under datasets/Criteo and rename it to full.txt.
• For Avazu dataset, copy the train.csv file under datasets/Avazu and rename it to full.csv.

Preprocess Dataset

• For Criteo dataset, run python preprocess/criteo.py
• For Avazu dataset, run python preprocess/avazu.py

Search

• For Criteo dataset, run python learn/CriteoSearch.py
• For Avazu dataset, run python learn/AvazuSearch.py

Re-train

• For Criteo dataset, run python learn/CriteoTrain.py --load XXX --model DNN_cart --alpha_mode 0
• For Avazu dataset, run python learn/AvazuTrain.py --load XXX --model DNN_cart --alpha_mode 0

Here XXX indicates the logs dictionary generated during the search stage.

Baseline Running

• For Criteo dataset, run python learn/CriteoTrain.py --model YYY
• For Avazu dataset, run python learn/AvazuTrain.py --model YYY

Here YYY could be {LR, FM, FNN, IPNN, DeepFM, PIN, Poly2, DNN_cart}

Citation

​```
@article{lyu2021memorize,
  title={Memorize, Factorize, or be Na$\backslash$" ive: Learning Optimal Feature Interaction Methods for CTR Prediction},
  author={Lyu, Fuyuan and Tang, Xing and Guo, Huifeng and Tang, Ruiming and He, Xiuqiang and Zhang, Rui and Liu, Xue},
  journal={arXiv preprint arXiv:2108.01265},
  year={2021}
}
​```