This repo is the TF2.0 implementation of perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online Games [PDF].
data/: data of perCLTV (randomly generated sample data to show the data format, not the real data).sample_data_individual_behavior.csv: the sample for individual behavior sequential data.sample_data_social_behavior.csv: the sample for social behavior graph data.sample_data_label.csv: the sample data for label, where label1 is churn label (binary classification) and label2 is payment label (regression).
src/: implementations of MSDMT.model.py: the code for model.
main.py: the code for pipeline.
The code has been tested running under Python 3.8.16, with the following packages installed (along with their dependencies):
- tensorflow == 2.12.0
- spektral == 1.2.0
- attention == 5.0.0
- numpy == 1.23.5
- pandas == 2.0.0
- scikit-learn == 1.2.2
$ python main.py
Please cite our paper if you use this code in your own work:
@article{zhao2023percltv,
title={perCLTV: A general system for personalized customer lifetime value prediction in online games},
author={Zhao, Shiwei and Wu, Runze and Tao, Jianrong and Qu, Manhu and Zhao, Minghao and Fan, Changjie and Zhao, Hongke},
journal={ACM Transactions on Information Systems},
volume={41},
number={1},
pages={1--29},
year={2023},
publisher={ACM New York, NY}
}