# Graph-Augmentation-Free Self-Supervised Learning for Social Recommendation
## 4 Experiments and Results
### Experimental Settings
#### 4.1.2 Baselines  
We evaluated GAFSRec against 13 baselines spanning different recommendations:  
* MF-based collaborative filtering models;
* BPR [23]: a popular recommendation model based on Bayesian personalized ranking;
* SBPR [28]: an MF-based social recommendation model that extends BPR and utilizes social relations to model the relative order of candidate items;
* GNN-based collaborative filtering frameworks;
* NGCF [7]: a complex GCN-based recommendation model that generates user/item representations by aggregating feature embeddings with high-order connection information;
* LightGCN [20]: a general recommendation model based on GCN, improved on the basis of NGCF by removing linear changes and activation functions;
* DiffNet++ [29]: a GNN-based social recommendation method that simultaneously simulates the recursive dynamic social diffusion of user space and item space;
* Recommendation with hypergraph neural networks;
* MHCN [9]: a social recommendation method based on a motif-based hypergraph. It aggregates high-level user information through hypergraph convolutional multi-channel embedding and utilizes auxiliary tasks to maximize the mutual information between nodes and graphs and generate self-supervised signals;
* HyperRec [13]: this method leverages the hypergraph structure to model the relationship between users and their interactive items by considering multi-order information in dynamic environments;
* HCCF [14]: a hypergraph-guided self-supervised learning recommendation model that jointly captures local and global collaborations through a hypergraph-enhanced cross-view contrastive learning architecture;
* Self-supervised learning for recommendation;
* SEPT [25]: a social recommendation model that utilizes multiple views to generate supervisory signals;
* SGL [8]: the most typical self-supervised comparative learning recommendation model, which uses structural perturbation to generate comparative views and maximizes the consistency between nodes. The experiment in this study used the structural perturbation method involving missing edges;
* BUIR [30]: this method adopts two encoders that learn from each other and randomly generates augmented views for supervised training;
* SimGCL [10]: the latest self-supervised contrastive learning recommendation model. It uses the no-image-enhancement method and only adds the final embedding obtained by adding uniform noise in the embedding space for comparison;
* NCL [31]: a prototypal structural contrastive learning recommendation model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Douban-Book** & **Yelp** & **Ciao** \\ \hline Users (U-I graph) & 12,859 & 19,539 & 7375 \\ Users (U-U graph) & 12,748 & 30,934 & 7317 \\ Items & 22,294 & 22,228 & 105,114 \\ Feedback & 598,420 & 450,884 & 2840,086 \\ Valid user pairs & 48,542,437 & 51,061,951 & 5,052,316 \\ Social pairs & 169,150 & 864,157 & 111,781 \\ Valid social pairs & 77,508 & 368,405 & 56,267 \\ Candidate user pairs & 165,353,881 & 381,772,521 & 54,390,625 \\ Valid ratio & 29.357\% & 13.375\% & 9.289\% \\ Valid social ratio & 45.822\% & 42.632\% & 50.337\% \\ Social density & 0.104\% & 0.090\% & 0.209\% \\ Rating density & 0.209\% & 0.104\% & 0.037\% \\ Social diffusivity level & 1.561 & 3.187 & 5.419 \\ Valid social density & 0.160\% & 0.721\% & 1.114\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data statistics for social recommendation datasets.