## 5 Experiments
### Experimental Results  
Comparative Analysis of Performance  
The empirical evaluation underscores the preeminence of DAGA across three benchmark datasets, as delineated in Tables 3 and 4. This section expounds on the comparative performance analysis, highlighting the salient findings and their implications. **Enhanced Performance of Attention-Based Models:** Among the evaluated baselines, models incorporating attention mechanisms, namely AGREE and MoSAN, consistently outshine conventional heuristic and collaborative filtering approaches such as Popular and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Weeplaces** & \multicolumn{2}{c}{**CAMRa2011**} & \multicolumn{2}{c}{**Douban**} \\
**Metric** & **R@20** & **R@50** & **R@20** & **R@50** & **R@20** & **R@50** \\ \hline \hline \multicolumn{6}{c}{**Baseline Recommender**} \\ \hline
**Popular** & 0.126 & 0.176 & 0.172 & 0.226 & 0.009 & 0.018 \\
**NeuMF** & 0.271 & 0.295 & 0.393 & 0.450 & 0.248 & 0.316 \\
**AGREE** & 0.354 & 0.671 & 0.529 & 0.688 & 0.297 & 0.488 \\
**MoSAN** & 0.548 & 0.738 & 0.572 & 0.801 & 0.384 & 0.459 \\
**SIGR** & 0.524 & 0.756 & 0.585 & 0.825 & 0.436 & 0.560 \\
**GroupIM** & 0.575 & 0.773 & 0.753 & 0.874 & 0.523 & 0.696 \\
**HHGR** & 0.554 & 0.764 & 0.703 & 0.830 & 0.507 & 0.677 \\
**\(S^{2}\)HHGR** & 0.592 & 0.797 & 0.779 & 0.883 & 0.561 & 0.741 \\
**SGGCF** & 0.689 & 0.794 & 0.789 & 0.891 & 0.573 & 0.744 \\ \hline \hline \multicolumn{6}{c}{**Our Recommender**} \\ \hline
**DAGA** & **0.723** & **0.814** & **0.801** & **0.905** & **0.608** & **0.771** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The recommendation (Recall) performance comparison on three datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Weeplaces** & \multicolumn{2}{c}{**CAMRa2011**} & \multicolumn{2}{c}{**Douban**} \\
**Metric** & **N@20** & **N@50** & **N@20** & **N@50** & **N@20** & **N@50** \\ \hline \hline \multicolumn{6}{c}{**Baseline Recommender**} \\ \hline
**Popular** & 0.063 & 0.074 & 0.099 & 0.122 & 0.003 & 0.005 \\
**NeuMF** & 0.193 & 0.244 & 0.305 & 0.367 & 0.124 & 0.167 \\
**AGREE** & 0.224 & 0.267 & 0.307 & 0.418 & 0.201 & 0.224 \\
**MoSAN** & 0.287 & 0.334 & 0.423 & 0.466 & 0.163 & 0.209 \\
**SIGR** & 0.357 & 0.391 & 0.499 & 0.524 & 0.217 & 0.235 \\
**GroupIM** & 0.431 & 0.456 & 0.637 & 0.659 & 0.257 & 0.284 \\
**HHGR** & 0.379 & 0.398 & 0.517 & 0.532 & 0.254 & 0.267 \\ \(S^{2}\)**HHGR** & 0.456 & 0.478 & 0.645 & 0.671 & 0.279 & 0.294 \\
**SGGCF** & 0.475 & 0.500 & 0.653 & 0.689 & 0.280 & 0.309 \\ \hline \hline \multicolumn{6}{c}{**Our Recommender**} \\ \hline
**DAGA** & **0.512** & **0.576** & **0.672** & **0.707** & **0.290** & **0.313** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The recommendation (NDCG) performance comparison on three datasets. NeuMF. This observation corroborates the pivotal role of attention mechanisms in adeptly capturing and aggregating individual preferences within groups. **Superiority of Graph-Based Representations:** The SIGR model, leveraging a bipartite graph structure, exhibits superior performance compared to AGREE and MoSAN in the majority of scenarios. This enhancement is attributed to SIGR's nuanced representation of user-group-item interactions, facilitated by the graph-based approach, underscoring the efficacy of graph embeddings in capturing complex relational dynamics. Moreover, the state-of-the-art method SGGCF, leveraging the heterogeneous graph, realizes more sufficient modeling to capture the relationships between users, groups and items, outperforming the aforementioned baselines. **Advantages of Mutual Information Optimization:** The GroupIM and SGGCF models, apart from harnessing attention-based aggregation, integrate mutual information (MI) SSL optimization objectives, endowing them with a distinct advantage over their counterparts. The MI objectives enrich the representation learning process, furthering the models' predictive accuracy.