## 5 Experiments
### Experimental Settings  
**Datasets:** To rigorously evaluate DAGA's performance, we employ three public benchmark datasets, Weekplaces, CAMRa2011 and Douban, each offering unique challenges and insights into ephemeral group recommendation dynamics. Table 2 summarizes key statistics of these datasets. * _Weeplaces_ captures point-of-interest (POI) interactions within major US cities, offering rich categorical data ranging from entertainment data to dining data. Groups are constructed based on simultaneous check-ins, aligning with previous methodologies [12];* _CAMRa2011_, a movie rating dataset, is adapted to the group recommendation context by focusing on households as implicit groups, with ratings transformed into binary interaction data for compatibility with our framework [11];
* _Douban_, sourced from a versatile social platform, facilitates the exploration of group dynamics within cultural contexts. Group interactions are inferred from shared participation in activities, following a strategy similar to that in [20]. For experimental rigor, each dataset is partitioned into training, validation and test sets in a 70:10:20 ratio, ensuring a comprehensive evaluation across diverse group recommendation scenarios. **Baselines:** The effectiveness of the proposed DAGA framework is evaluated against a diverse array of baseline methods, encompassing traditional popularity-based approaches, neural collaborative filtering techniques and advanced group recommendation models incorporating attention mechanisms and graph embeddings. The following selected baselines are included:  
* _Popular:_ A rudimentary yet widely applied method that recommends items based solely on their overall popularity, disregarding personalized preferences or group dynamics;
* _NeuMF_[57]: NeuMF represents the integration of neural network architectures into collaborative filtering, enhancing recommendation performance by directly leveraging group-item interactions as the supervisory signal. NeuMF exemplifies the advancements in neural collaborative filtering by predicting group preferences with heightened accuracy;
* _AGREE_[11]: AGREE innovates by incorporating an attention mechanism to deduce group preferences from individual member preferences. It computes weights for each group member and aggregates them to obtain a unified group preference vector, employing a neural collaborative filtering framework to model group-item interactions;
* _MoSAN_[18]: An advanced group recommendation model that utilizes a neural network structure with multiple sub-attention modules. These modules are tasked with capturing nuanced user preferences and effectively modeling the intricate dynamics of user-item and group-item interactions;
* _SIGR_[19]: SIGR introduces an attention-based model augmented with latent variables to capture both global and local social influences among users. It conceptualizes interactions within a bipartite graph structure, applying graph embedding techniques to mitigate issues related to data sparsity;
* _GroupIM_[12]: A cutting-edge group recommendation model that aggregates individual member preferences through attention networks to forecast group preferences. It distinguishes itself by optimizing the mutual information between group and member representations, addressing the challenge of sparse data;
* _HHGR_[20]: HHGR utilizes a hypergraph neural network to encapsulate the complex high-order relationships among users, groups and items. It is particularly noted for its innovative double-scale self-supervised learning objective, designed to complement traditional supervised learning signals and enhance the training process;
* _S\({}^{2}\)HHGR_[20]: An extension of the HHGR model, this self-supervised variant represents the state of the art in group recommendation.