SSLRec is a PyTorch-based deep learning framework for recommender systems enhanced by self-supervised learning techniques. It contains commonly-used datasets, code scripts for data processing, training, testing, evaluation, and state-of-the-art research models.

SSLRec is implemented under the following development environment:

• python==3.10.4
• numpy==1.22.3
• torch==1.11.0
• scipy=1.7.3

To run a specific methods, change your directory to the corresponding directory (e.g. methods/sgl/). Run the training and testing with this command line: python Main.py. Some important arguments shared by most methods are as follows:

• data: This is a string arguments specifying which dataset to run on. Currently we have released six datasets: sp_gowalla, sp_yelp, sp_amazon, yelp, ml10m and amazon.
• latdim: This denotes the dimensionality of latent embeddings. By default it is set as 32 for all methods.
• gnn_layer: For GNN-based methods, this parameter determines the number of GNN layers.
• reg: This parameter determines the weight for weight-decay regularization ($l_2$ regularization).
• ssl_reg: For most methods that utilize only one SSL training objective, this parameter specify the weight for SSL loss term.
• temp: For most methods that utilize only one SSL training objective, this parameter specify the temperature factor for SSL.
• keepRate: For methods that conduct random drop, this parameter specify the rate to keep values.