Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems

In this work, we propose a solution integrating a cutting-edge model inspired by category theory: Sheaf4Rec. Our approach takes advantage from sheaf theory and results in a more comprehensive representation that can be effectively exploited during inference. Our proposed model exhibits a noteworthy relative improvement of up to 8.53% on F1-Score@10 and an impressive increase of up to 11.29% on NDCG@10, outperforming existing state-of-the-art models such as NGCF, KGTORe and other recently developed GNN-based models. Sheaf4Rec shows remarkable improvements in terms of efficiency: we observe substantial runtime improvements ranging from 2.5% up to 37% when compared to other GNN-based competitor models.

All the code is written in Python and is based on Pytorch, Pytorch Geometric and the use of Wandb for logging purposes.

Setup

Clone the repo:

git clone https://github.com/antoniopurificato/Sheaf4Rec.git && cd Sheaf4Rec

Before running the code you have to download the datasets and to insert them in the right folder.

cd data

To download MovieLens 1M:

wget https://files.grouplens.org/datasets/movielens/ml-1m.zip && unzip ml-1m.zip

The Facebook dataset is in the data folder. For the Yahoo! Movies you have to request the access here.

Return to project directory:

cd .. 

After you clone this repo you have to create a conda environment.

conda create -n sheaf_rec python=3.8 && conda activate sheaf_rec && conda install pip

Move the current directory to this repository and install the required packages:

conda activate sheaf_rec && pip install -r requirements.txt

Run the following command:

pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cu117.html

To run an experiment:

python3 train.py --epochs [NUMBER OF EPOCHS] --dataset [DATASET NAME] --K_list [10,20]  --run_name [RUN NAME] --layers [NUMBER OF LAYERS] --gpu_id [ID OF THE GPU] --learning_rate [LEARNING RATE] -- entity_name[NAME OF THE ENTITY IF YOU HAVE A SHARED PROJECT IN WANDB, 0 OTHERWISE] --project_name [NAME OF THE ENTITY IN WANDB] --model [MODEL NAME] --log_metrics [LOGS FOR STATISTICAL TESTS] --latent_dim [LATENT DIMS]

You can use this command line to have a first simple run:

python train.py --epochs 100 --dataset yahoo --run_name Example_run_name --layers 5 --gpu_id 1 --model sheaf

Remember that to run this code it is necessary to use Wandb.

It is also possible with this code to run a sweep on Wandb to optimize the hyperparameters. There is a simple example of sweep in mysweep.yaml.

REMINDER! Your current directory must always be Sheaf4Rec , otherwise you'll have problems with the code!

References

Sheaf Neural Networks for Graph-based Recommender Systems

[1] Purificato Antonio, Cassarà Giulia, Liò Pietro, Silvestri Fabrizio Sheaf Neural Networks for Graph-based Recommender Systems

@article{purificato2023sheaf,

title={Sheaf Neural Networks for Graph-based Recommender Systems},
  
author={Purificato, Antonio and Cassarà, Giulia and Liò, Pietro and Silvestri, Fabrizio},

journal={arXiv preprint arXiv:2304.09097},
  
year={2023}
}

Sheaf Neural Networks with Connection Laplacians

[2] Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael Bronstein, Petar Veličković, Pietro Liò Sheaf Neural Networks with Connection Laplacians

@misc{barbero2022sheaf,

title={Sheaf Neural Networks with Connection Laplacians}, 
      
author={Federico Barbero and Cristian Bodnar and Haitz Sáez de Ocáriz Borde and Michael Bronstein and Petar Veličković and Pietro Liò},
      
year={2022},
      
eprint={2206.08702},
      
archivePrefix={arXiv},
      
primaryClass={cs.LG}
}