DEFUSE

Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction

<!-- ### Citation Please cite this paper if you used any content of this repo in your work: ```tex @article{DBLP:journals/corr/abs-2202-06472, author = {Yu Chen and Jiaqi Jin and Hui Zhao and Pengjie Wang and Guojun Liu and Jian Xu and Bo Zheng}, title = {Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction}, journal = {CoRR}, volume = {abs/2202.06472}, year = {2022}, url = {https://arxiv.org/abs/2202.06472}, eprinttype = {arXiv}, eprint = {2202.06472}, timestamp = {Fri, 18 Feb 2022 12:23:53 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2202-06472.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` -->

Environment Requirement

The code has been tested running under Python 3.8.10. The required packages are follows:

• numpy==1.18.5
• tqdm==4.61.2
• pandas==1.3.1
• scikit_learn==1.0.2
• tensorflow==2.4.1

Example to Run the Codes

The instruction of commands has been clearly stated in the shell scripts: We uploaded some shell scripts as a reference to run the code, however, the pathes should be modified accordingly.

• run_pretrain.sh:

pretrain ckpts using the first 30 days information. we also provide the model checkpoints to reproduce the results on the public Criteo30d.

• run_base(_1d).sh

obtain baseline results under streaming setting.

• run_defuse.sh

obtain our DEFUSE results.

Dataset

The criteo dataset is available at https://drive.google.com/file/d/1x4KktfZtls9QjNdFYKCjTpfjM4tG2PcK/view?usp=sharing

A preprint version of this paper is available at https://arxiv.org/abs/2202.06472.pdf