## 2 Scientific Method
### Group Recommendation  
Group recommendation methodologies have been conceptualized from dual perspectives to cater to two distinct audience types: ephemeral [12; 23] and persistent [19] groups. The latter category, characterized by dense group-item interaction records, readily lends itself to the application of collaborative filtering techniques directly to group-item interactions [10; 24]. The focus of this paper, however, is on the more pragmatically challenging domain of ephemeral group recommendations, a subject that has received considerable attention in the recent literature [12; 20; 21]. Within this domain, the primary challenge lies in the paucity of group-item interaction data [12; 25]. Consequently, this review is bifurcated into two streams: methods for group preference aggregation and strategies to counteract data sparsity. **Preference Aggregation:** In the realm of ephemeral group recommendations, the synthesis of group preferences is achieved by amalgamating individual member preferences, which can be executed either at an early or late stage. Late-stage aggregation involves deriving group-item recommendation scores after the individual preference processing, employing strategies such as the maximum pleasure, least misery and average satisfaction strategies [13; 16; 23]. These traditional approaches, however, are heavily heuristics dependent, which compromises their adaptability and expressive capabilities [26; 27]. Early aggregation, conversely, entails the initial consolidation of individual preferences to formulate a collective group preference, which is subsequently utilized for recommendation generation. Probabilistic models [28; 29; 30] form the basis of several such methodologies, assuming uniform significance across members while accounting for individual preferences and their contributions to the group dynamic. Advanced latent factor models [31; 32] facilitate group embedding within a vectorial space, enhancing representational granularity. Further sophistication is achieved through the adoption of attention-based neural networks [11; 26], enabling nuanced aggregation of biased user embeddings within a group context. Additionally, a model capable of delineating the intricate inter-member relationships via attention mechanisms has been introduced [18]. Contemporary studies have extended these methodologies by integrating ancillary information sources such as social networks [33] and knowledge graphs [34], thereby enriching the learning paradigms for group representation. Innovations such as hypercube-based representations [21] and hypergraph convolutional neural networks [20] have been explored to encapsulate complex relational dynamics within and beyond group entities. Moreover, the group identification (GI) task, aiming to recommend the potential group to users, has recently attracted more attention from researchers. As a representative work among them, CFAG [35] was the first to propose using GNNs with factorized attention block to aggregate contextual information among the items, groups and users so as to perform ranking-based GI tasks. GTGS [36] designed a transitional hypergraph convolution-layer-based framework and leveraged the cross-view SSL method to predict the preferences of users for groups based on the users' previous group participation and coordinated interests in items. **Data Sparsity Issue:** Addressing the challenge of sparse data, the influence of user social networks on group decision-making [19] has been leveraged to augment the available dataset. Innovations such as self-attention mechanisms [37] have been employed in intra-group voting models [26] to concurrently assess user interactions and social influences. Additionally, methodologies that construct heterogeneous graphs from multifaceted relationships among users, items and groups have been proposed [17; 22].