###### Abstract  
Group recommender systems aim to provide recommendations to a group of users as a whole rather than to individual users. Nonetheless, prevailing methodologies predominantly aggregate user preferences without adequately accounting for the unique individual intents influencing item selection. This oversight becomes particularly problematic in the context of ephemeral groups formed by users with limited shared historical interactions, which exacerbates the data sparsity challenge. In this paper, we introduce a novel Disentangled Self-Attention Group Recommendation framework with auto-regressive contrastive learning method, termed DAGA. This framework not only employs disentangled neural architectures to reconstruct the multi-head self-attention network but also incorporates modules for mutual information optimization via auto-regressive contrastive learning to better leverage the context information of user-item and group-item historical interactions, obtaining group representations and further executing recommendations. Specifically, we develop a disentangled model comprising multiple components to individually assess and interpret the diverse intents of users and their impacts on collective group preferences towards items. Building upon this model, we apply the principle of contrastive mutual information maximization to train our framework, aligning the group representations with the corresponding user representations derived from each factor of the disentangled model, thereby enriching the contextual understanding required to effectively address the challenges posed by ephemeral groups. Empirical evaluations conducted on three real-world benchmark datasets substantiate the superior performance of our proposed framework over existing state-of-the-art group recommendation approaches. g 2024 14 4155 10.10.  
the mutual preference of [PERSON] and [PERSON] may lead them to choose fast food for convenience. In addition, there are many application domains that would benefit from group recommender systems. For example, on some e-commerce platforms like pinduouduo, in order to sell goods in bulk, it is necessary to recommend items for a temporary user group who is willing to buy at a slightly lower price. In another academic scenario, an institution needs to recommend a journal for a group of scholars attending a famous conference to improve the impact of this journal. Accordingly, understanding the intricate dynamics within and across groups, as well as their contributions to collective decision-making, is crucial for enhancing group recommendation systems. The existing literature [3; 4; 5; 6; 7; 8; 9] has predominantly concentrated on personalized recommendations, with a significant emphasis on methodologies grounded in collaborative filtering. However, the domain of group recommendation [10; 11; 12] has been garnering escalating interest in recent scholarly endeavors. A considerable segment of this research is dedicated to elucidating methodologies for the derivation of group representations, which are subsequently utilized for inferential purposes. These methodologies frequently resort to heuristic rules for the aggregation of individual user features into a cohesive group representation, with prevalent approaches including mean pooling [13; 14], the maximum principle [15] and the least misery strategy [16]. Parallel to the ascent of deep learning paradigms and attention mechanisms, a number of innovative approaches [17; 18; 19] have emerged, incorporating attention-based modules within user-item bipartite graphs to effectively harness user interactions for the formulation of group preference representations. The recent discourse has progressively shifted towards an examination of the representational capacities of these frameworks. In this vein, GroupIM [12] has emerged as a pioneering initiative aimed at augmenting performance through architectural innovations and refined optimization objectives.