This repo is the pytorch implementation of Multi-source Data Multi-task Learning for Profiling Players in Online Games (MSDMT) [PDF].
It is adapted from the TF2.0 implementation (https://github.com/fuxiAIlab/MSDMT)
The description of the authors: MSDMT is a novel Multi-source Data Multi-task Learning approach for profiling players with both player churn and payment prediction in online games. On the one hand, MSDMT considers that heterogeneous multi-source data, including player portrait tabular data, behavior sequence sequential data, and social network graph data, can complement each other for a better understanding of each player. On the other hand, MSDMT considers the significant correlation between the player churn and payment that can interact and complement each other.
data/: data of MSDMT (randomly generated sample data to show the data format, not the real data).sample_data_player_portrait.csv: the sample data for player portrait.sample_data_behavior_sequence.csv: the sample data for behavior sequence.sample_data_social_network.csv: the sample data for social network.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 in pytorch.model.py: the code for model.main.py: the code for pipeline.
src-tf/: implementations of MSDMT in TensorFlow 2.0.model.py: the code for model.main.py: the code for pipeline.
Core libraries:
- numpy
- pandas
- scikit-learn
- networkx
- torch
Other libraries:
- torch-scatter
- torch-sparse
- torch-cluster
- torch-spline-conv
- torch-geometric
$ cd src
$ python main.py
The authors indicated the following dependencies: The code has been tested running under Python 3.5.2, with the following packages installed (along with their dependencies):
- tensorflow == 2.1.0
- spektral ==1.0.3
- numpy == 1.18.2
- pandas == 0.23.4
- sklearn == 0.19.1
However I have not tested it with their settings, and it might be deprecated
$ cd src-tf
$ python main.py