Awesome
RHINE
Source code for CIKM 2021 paper "Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation"
Requirements
- Python 3.8
- PyTorch (1.9.1)
- numpy (1.19.2)
- pandas (1.2.4)
- scipy (1.6.2)
- sklearn (0.24.2)
Description
HHGR-s2/
├── models
│ ├── HHGR.py: the main model with some functions and configs for the model
│ ├── HGCN.py: the hypergraph convolutional network model
│ ├── Discriminator.py: discriminator network model for self-supervised learning
│ ├── EmbeddingLayer.py: Embedding network model for learning the representations of group, user, and item
├── utils
│ ├── util.py: evaluate the performance of learned embeddings w.r.t clustering and classification
│ ├── dataset.py: generate the group and user dataloader
│ ├── user_tuils.py: generate the user dataloader for training the model
│ ├── group_tuils.py: generate the group dataloader for training the model
├── data
│ └── weeplaces
│ ├── group_users.csv: the group-user relationship
│ ├── train_ui.csv: the training file of user-item history interaction
│ ├── train_gi.csv: the training file of group-item history interaction
│ ├── val_ui.csv: the validation file of user-item history interaction
│ ├── val_gi.csv: the validation file of group-item history interaction
│ ├── test_ui.csv: the test file of user-item history interaction
│ ├── test_gi.csv: the test file of user-item history interaction
├── README.md
Reference
@article{DBLP:journals/corr/abs-2109-04200,
author = {Junwei Zhang, Min Gao, Junliang Yu, Lei Guo, Jundong Li, and Hongzhi Yin},
title = {Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation},
booktitle={Proceedings of CIKM},
year = {2021},
}