This is the official PyTorch implementation for the paper:
- [UFIN: Universal Feature Interaction Network for Multi-Domain Click-Through Rate Prediction]
We propose the Universal Feature Interaction Network (UFIN) approach for CTR prediction. UFIN exploits textual data to learn universal feature interactions that can be effectively transferred across diverse domains. For learning universal feature representations, we regard the text and feature as two different modalities and propose an encoder-decoder network founded on a Large Language Model (LLM) to enforce the transfer of data from the text modality to the feature modality. Building upon the above foundation, we further develop a mixture-of-experts (MoE) enhanced adaptive feature interaction model to learn transferable collaborative patterns across multiple domains. Furthermore, we propose a multi-domain knowledge distillation framework to enhance feature interaction learning. Based on the above methods, UFIN can effectively bridge the semantic gap to learn common knowledge across various domains, surpassing the constraints of ID-based models. Extensive experiments conducted on eight datasets show the effectiveness of UFIN, in both multi-domain and cross-platform settings.
tensorflow==2.4.1
python==3.7.3
cudatoolkit==11.3.1
pytorch==1.11.0
transformers
To evaluate the performance of our model, we conduct experiments on the Amazon and MovieLens-1M datasets. The scripts for dataset processing can be found under the /DataSource folder. You first need to download the raw dataset files and put them into the /DataSource folder.
Then pre-process the data:
python DataSource/[dataset]_parse.py
[Note: We also provide the processed dataset at this link: https://drive.google.com/file/d/1kMsnxyEnx2ZrAqjk9L3Ejypkt7HqYGgB/view?usp=sharing, where you can directly download these files.]
Finally, get the files for training, validation, and testing:
bash split_all.sh
bash pretrain_teacher.sh
python train.py --config_files=UFIN.yaml
bash test_all.sh