# Graph-Augmentation-Free Self-Supervised Learning for Social Recommendation
## 4 Experiments and Results
### Recommendation Performance
#### 4.2.2 Comparison of Ability to Promote Long-Tail Items  
As mentioned in the introduction, GNN-based recommendation models are easily affected by the long-tail problem. To verify that GAFSRec could entirely alleviate the long-tail problem, we divided the test set into ten groups according to popularity. Each group contained the same numbers of interactions, and the larger the ID of a group was, the more popular the items were. Then, we set the number of layers to two for the experiments and verified the long-tail recommendation ability of the model by observing Recall@20.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Dataset** & **Method** & **BPR** & **SSPR** & **NGCF** & **LightGCN** & **DiffNET++** & **MICN** & **HCCF** & **SOL** & **SimGCL** & **NCL** & **GAFSRec** & **Improvement** \\ \hline \multirow{2}{*}{Doubunan-Book} & Recall@20 & 0.0754 & 0.1045 & 0.1135 & 0.1108 & 0.1105 & 0.1755 & 0.1877 & 0.1929 & 0.1924 & 0.1725 & **0.1972** & 2.79\% \\  & NDCC@20 & 0.0888 & 0.0879 & 0.0644 & 0.0631 & 0.0590 & 0.1043 & 0.1098 & 0.1154 & 0.1164 & 0.1024 & **0.1233** & 5.94\% \\ \hline \multirow{2}{*}{Yelp} & Recall@20 & 0.0619 & 0.0696 & 0.0881 & 0.0881 & 0.0917 & 0.1258 & 0.1271 & 0.1322 & 0.1212 & 0.1150 & **0.1330** & 5.89\% \\  & NDCC@20 & 0.0296 & 0.0427 & 0.0415 & 0.0402 & 0.0420 & 0.0610 & 0.0662 & 0.0709 & 0.0625 & 0.0571 & **0.0669** & 11.57\% \\ \hline \multirow{2}{*}{Ciao} & Recall@20 & 0.0416 & 0.0413 & 0.0576 & 0.0544 & 0.0519 & 0.0638 & 0.0638 & 0.0605 & 0.0627 & 0.0635 & **0.0731** & 14.51\% \\  & NDCC@20 & 0.0209 & 0.0251 & 0.0330 & 0.0324 & 0.0308 & 0.0340 & 0.0331 & 0.0338 & 0.0339 & 0.0338 & **0.0374** & 9.82\% \\ \hline \end{tabular}
\end{table}
Table 4: Cold-start recommendation performance comparison.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Dataset** & **Metric** & **BPR** & **SSPR** & **NGCF** & **LightGCN** & **DiffNET++** & **HyperRec** & **MICN** & **HCCF** & **BUIR** & **SLRec** & **SGL** & **SimGCL** & **NCL** & **GAFSRec** & **Improvement** \\ \hline \multirow{2}{*}{Douban-Book} & Recall@20 & 0.0889 & 0.0918 & 0.1167 & 0.0936 & 0.0988 & 0.1394 & 0.1487 & 0.1666 & 0.0892 & 0.1400 & 0.1617 & 0.1629 & 0.1650 & **0.1781** & 6.90\% \\  & Recall@20 & 0.0882 & 0.0717 & 0.0943 & 0.0770 & 0.0791 & 0.1260 & 0.1329 & 0.1421 & 0.0793 & 0.1263 & 0.1412 & 0.1422 & 0.1416 & **0.1637** & 15.17\% \\ \hline \multirow{2}{*}{Yelp} & Recall@20 & 0.0554 & 0.0665 & 0.0891 & 0.0820 & 0.0852 & 0.1120 & 0.1174 & 0.1157 & 0.0834 & 0.1126 & 0.1146 & 0.1145 & 0.1148 & **0.1292** & 12.37\% \\  & NDCC@20 & 0.0289 & 0.0403 & 0.0533 & 0.0472 & 0.0496 &As shown in Figure 4, LightGCN tended to recommend popular items and had the lowest recommendation ability for long-tail items (as illustrated in the small figure). Due to sparse interaction signals, it was difficult for LightGCN to obtain high-quality representations of long-tail items. However, with SimGCL, by optimizing the consistency of InfoNCE-loss learning representations, long-tail problems could be avoided as much as was possible, and excellent recommendation performance could be achieved. MHCN also optimized the InfoNCE loss through global and local comparisons but did not learn a more consistent representation, resulting in inferior performance to SimGCL when recommending long-tail items. In contrast, GAFSRec showed outstanding advantages when recommending long-tail products (such as GroupIDs 1, 2, and 3) and the highest recall value, but it was not as good as other models for GroupID 10. It can be seen that learning a more uniform representation by optimizing the InfoNCE loss can enable models to debias and alleviate the long-tail phenomenon, as well as increasing freshness and helping to meet user needs.