Hi! This is the source code for TAG-CF under review at ICLR 2024. Thanks for your interest in our project and we sincerely aprpeciate your efforts in reading or reviewing our paper. 

We use [RecBole](https://recbole.io) to conduct all baseline experiments and development of TAG-CF. Due to space restrictions, we only provide anime checkpoints to showcase our performance. To calculate the performance for Anime, simply run

```
python tagcf.py
```

Below should be the output:

```
Preparing Datasets...
Downloaded 0.02 GB: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 27/27 [00:02<00:00, 11.70it/s]
Running TAG-CF...
TAG-CF results: NDCG@20:0.27241951226466005, Recall@20:0.3191512133087205
Running MF...
MF results: NDCG@20:0.24017929631786855, Recall@20:0.29030158705033743
Running LightGCN...
LightGCN results: NDCG@20:0.2794491635454047, Recall@20:0.3279140864902523
```

To run our code, please install RecBole first from its [GitHub repo](https://github.com/RUCAIBox/RecBole). 