# AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation
## 5 Experiments
### Overall Performance  
In this section, we conduct a comprehensive performance comparison of asGCL against various baselines across four datasets. Additionally, we retrain asGCL and the best-performing baseline five times to compute \(p\)-values. The results are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Dataset** & **Metrics** & **BPRMF** & **NeuMF** & **GCMC** & **GAT** & **NGCF** & **LightGCN** & **SGL** & **LightGCL** & **asGCL** & \(p\)-Val. ** & **Improv. ** \\ \hline \multirow{2}{*}{Amazon-Book} & R@20 & 0.0764 & 0.0575 & 0.0711 & 0.0729 & 0.0760 & 0.0954 & 0.1002 & 0.1021 & **0.1064** & \(1.4\times e^{-2}\) & 4.21\% \\  & N@20 & 0.0474 & 0.0345 & 0.0557 & 0.0574 & 0.0472 & 0.0614 & 0.0659 & 0.0675 & **0.0734** & \(1.0\times e^{-3}\) & 8.874\% \\ \hline \multirow{2}{*}{Gowalla} & R@20 & 0.1686 & 0.1470 & 0.1485 & 0.1523 & 0.1633 & 0.1882 & 0.2109 & 0.2112 & **0.2197** & \(5.3\times e^{-4}\) & 4.02\% \\  & N@20 & 0.0965 & 0.0808 & 0.0812 & 0.0857 & 0.0937 & 0.1099 & 0.1134 & 0.1133 & **0.1203** & \(1.1\times e^{-6}\) & 6.11\% \\ \hline \multirow{2}{*}{Yelp2018} & R@20 & 0.0764 & 0.0548 & 0.0915 & 0.0939 & 0.0778 & 0.0914 & 0.0947 & 0.0889 & **0.0990** & \(3.0\times e^{-3}\) & 4.63\% \\  & N@20 & 0.0460 & 0.0316 & 0.0488 & 0.0513 & 0.0472 & 0.0567 & 0.0588 & 0.0592 & **0.0614** & \(2.3\times e^{-2}\) & 3.71\% \\ \hline \multirow{2}{*}{ML-1M} & R@20 & 0.2637 & 0.2455 & 0.2611 & 0.2656 & 0.2695 & 0.2647 & 0.2697 & 0.2717 & **0.2872** & \(5.3\times e^{-6}\) & 5.70\% \\  & N@20 & 0.2464 & 0.2267 & 0.2464 & 0.2473 & 0.2483 & 0.2511 & 0.2553 & 0.2530 & **0.2 We can draw the following experimental conclusions:  
* We propose that asGCL consistently outperforms all baseline models across various datasets and metrics. Specifically, asGCL achieves relative improvements of 19.5% and 9.46% over LightGCN on the Amazon-Books and Gowalla datasets, respectively, in terms of NDCG@20. On the Yelp2018 and ML-1M datasets, asGCL shows relative enhancements of 8.31% and 8.50% over LightGCN in Recall@20. Compared to the strongest baseline model, LightGCL, asGCL also demonstrates superior performance, with Recall@20 improvements of 4.21%, 4.02%, 11.36%, and 5.70% and NDCG@20 improvements of 8.74%, 6.17%, 3.71%, and 5.41% across the same datasets. Furthermore, models based on graph convolution networks generally outperform those based on matrix factorization, as evidenced by LightGCN's significant advantage over previous baselines. Additionally, graph contrastive learning methods, such as SGL and LightGCL, exhibit performance gains over LightGCN across all four datasets. * Represented by LightGCN and BPR, GNN-based methods outperform matrix factorization methods, indicating the positive role of higher-order node information in the learning process. As noted, asGCL demonstrates the best performance across all four datasets. Specifically, on the Amazon-Books dataset, asGCL achieves improvements of 4.21% in recall@20 and 8.74% in NDCG@20, which clearly indicates a significant advancement in our model's performance. The incorporation of weight information for nodes along with contrastive learning methods in asGCL represents an effective research direction. Summarizing the impressive results exhibited by our model, we attribute this performance to the following factors. (1) The lightweight attention mechanism of asGCL further amplifies the differences among nodes. (2) Compared to other baseline models, the contrastive learning method introduced by asGCL indeed brings considerable performance improvements. This approach contrasts across layers with uniform noise and is more inclined towards representation learning rather than graph enhancement, as we believe that uniform node distribution and the contrastive loss function are crucial factors contributing to the effectiveness of contrastive learning. * Compared to GCN, collaborative filtering methods that employ contrastive learning models demonstrate significant performance improvements across nearly all metrics.