###### Abstract
Subsequently, the HHGR model [20] was introduced, utilizing a hypergraph framework to encapsulate the intricate higher-order relationships among users, both within and external to group contexts. Furthermore, CubeRec [21] ventured into employing a hypercube structure for articulating group preferences within the embedding space, thereby imbuing the model with enhanced flexibility and the capability to navigate the complex dynamics of user-group interactions. Recently, SGGCF [22] was proposed to capture the high-order interactions between users, items and groups by modeling with a user-centered heterogeneous graph and a self-supervised learning framework. Despite the remarkable achievements of existing methodologies such as the aforementioned CF-based methods, GroupIM, HHGR, CubeReC and SGGCF, they are often  
Figure 1: An example of group recommendation. encumbered by certain limitations as follows. Predominantly, when delineating user or group preferences and underlying intentions, they resort to a hybrid modeling approach utilizing conventional neural networks like MLP, attention layers or GNNs, resulting in a lack of fine-grained modeling of relationships between the factor elements of the user/group interest preferences. This means that they are not applicable for inherently complex and multifactorial recommendation tasks, which demand a more fine-grained representation for the modeling approach to adequately represent user and group preferences. To be more specific, we illustrate this with the example in Figure 1. The figure shows a typical traditional model which constructs a neural network that translates input features--reflecting the original culinary preferences of users or groups--into embeddings that signify a synthesized decision-making probability distribution for potential meal options. This approach tends to oversimplify by directly targeting the gastronomic preferences of users or groups without accounting for detailed attributes such as the selection of main courses, side dishes or beverages. Furthermore, the ephemeral nature of certain group recommendations exacerbates the issue of data sparsity. Given that ephemeral groups often comprise individuals with minimal shared historical activities, there is a paucity of meaningful group-item interaction data to accurately reflect the preferences and intentions of such groups, leading to diminished recommendation performance. This lack of historical data particularly hampers neural-network-based group recommendation architectures as the scarcity of supervised signals hinders optimal network training. According to these challenges, i.e., the lack of effective methodologies to represent user/group preferences with fine-grained modeling and data sparsity issues, we propose a disentangled self-attention-based neural framework to adequately model user/group potential factors of preferences with more fine-grained representations and propose auto-regressive user preferences and mutual information optimization objectives. Specifically, we introduce a novel Disentangled Self-Attention Group Recommendation framework with an auto-regressive contrastive learning method (DAGA), designed to derive nuanced user and group representations and facilitate item suggestions based on historical user-item and group-item interactions and group-related mutual information. To address the limitations of synthetic modeling, we establish distinct factors, each represented by a separate neural network module, to encapsulate potential user/group factors. These modules allow attribute-specific input features to be processed accordingly, with the resulting representations integrated for inferential purposes. On this basis, a disentangled multi-head self-attention module is designed to better encode the user preferences assisted by the preference features of the other individuals of the same group so as to produce more accurate group representations via an attention-based aggregator and further execute group recommendation.