## 5 Experiments
### Experimental Settings
It leverages self-supervised learning techniques to further refine the model's ability to discern and predict group preferences;
* _SGGCF_[22]: SGGCF captures the high-order interactions between users, items and groups by modeling with a user-centered heterogeneous graph and a self-supervised \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **\#Users (\#U)** & **\#Items (\#I)** & **\#Groups (\#G)** & **\#U-I Interact** & **\#G-I Interact** \\ \hline Weeplaces & 8643 & 25,081 & 22,733 & 1,358,458 & 180,229 \\ CAMRa2011 & 602 & 7710 & 290 & 116,344 & 145,068 \\ Douban & 2964 & 39,694 & 2630 & 823,851 & 463,040 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of the evaluated datasets. # represents the number of the indicators. learning framework. SGGCF realizes a state-of-the-art performance for ephemeral group recommendation. Each baseline is selected for its relevance and contribution to the field of group recommendation, providing a comprehensive benchmark against which the performance of DAGA is evaluated. Official implementations of these models, where available, are utilized to ensure fidelity to the original methodologies. **Evaluation Metrics:** The efficacy of DAGA and the compared baseline models is quantified using two widely recognized metrics in the domain of recommendation systems: _Normalized Discounted Cumulative Gain at K (NDCG@K)_ and _Recall at K (Recall@K)_ for \(K=\{20,50\}\). These metrics are selected for their ability to provide a comprehensive assessment of the models' performance from different perspectives. * _NDCG@K_ is a metric that measures the quality of the ranked recommendation list up to the K-th position, taking into account the position of relevant items. It is particularly well suited for evaluating recommendation systems where the order of recommendations is of importance. The computation of NDCG@K involves the gain of each item (with more weight given to items at higher ranks) and a normalization factor to ensure that the perfect ranking has a score of 1. This makes NDCG@K an effective measure for understanding how well a model can rank truly relevant items higher in the recommendation list;
* _Recall@K_, on the other hand, assesses the model's ability to retrieve relevant items within the top K recommendations, regardless of their order. It is calculated as the fraction of relevant items that are successfully included in the top K recommended items out of all relevant items. Recall@K is particularly useful in scenarios where the goal is to capture as many relevant items as possible in the top K recommendations without an emphasis on the ranking order among these items. Both NDCG@K and Recall@K provide valuable insights into the model performance, with NDCG@K focusing on the ranking quality of the recommendations and Recall@K emphasizing the model's ability to identify relevant items. Employing both metrics allows for a balanced evaluation of the recommendation models, catering to different aspects of recommendation quality that are crucial in real-world applications. **Parameter Settings:** The configuration of hyperparameters plays a pivotal role in the performance and efficiency of the DAGA framework. To establish a robust experimental foundation, we meticulously select and fine-tune the following hyperparameters:  
* _Learning Rate:_ Set at \(5\times 10^{-3}\), the learning rate determines the step size during the gradient descent optimization process. This value is chosen to balance the trade-off between convergence speed and stability;
* _Weight Decay:_ A regularization parameter employed to prevent overfitting by penalizing large weights. For our experiments, weight decay is set to 0, emphasizing model flexibility in learning from the data;
* _Dropout Ratio:_ Applied within the MLPs to mitigate overfitting through the random omission of neurons during training.