## 6 Conclusions  
In this work, we introduce a novel ephemeral group recommendation framework, termed DAGA, which employs disentangled neural networks for self-attention-based and MLP-based encoder design in conjunction with a self-supervised learning strategy  
Figure 3: Performance of variations of DAGA across embedding dimension sizes (16 to 192) and factor numbers (1 to 16) on the Weeplaces dataset.  
predicated on mutual information to intricately model user intents and address issues of data sparsity.
* Theoretical Contributions: The innovative disentanglement approach allows for the independent transfer of user and group representations across distinct factors in the self-attention mechanism and MLP encoders, effectively mitigating the amalgamation of unrelated factor information. To facilitate the training of this framework, we formulate both supervised and self-supervised loss functions. These functions capitalize on the preference probability distributions at the input and output stages of DAGA, as well as on the contextual insights gleaned through the maximization of mutual information pertaining to group representations;
* Applied Contributions: Comprehensive empirical analyses conducted on three publicly available benchmark datasets substantiate the superior performance of DAGA in comparison to the state-of-the-art methodologies. Given the demonstrated efficacy of collaborative filtering (CF) in recommendation systems, our future endeavors will focus on the exploration of group recommendation models predicated on CF techniques and the development of more nuanced objective functions to further enhance model performance;
* Main Limitations: Additionally, there are also some limitations to our proposed framework. For example, DAGA performs best when the training flow follows the training procedure as shown in Algorithm 1. However, when it comes to an end-to-end training procedure, there will be some performance degradation. In our future work, we will explore the more efficient end-to-end training framework for group recommendation and also want to discover more applicable scenarios for the multi-head disentangled self-attention encoder, including a variety of recommended and non-recommended tasks.  
Conceptualization, L.G., H.Z. and L.F.; methodology, L.G., H.Z. and L.F.; software, L.G. and H.Z.; validation, L.G. and H.Z.; formal analysis, L.G., H.Z. and L.F.; investigation, L.G.; resources, H.Z.; data curation, L.G.; writing--original draft preparation, L.G., H.Z. and L.F.; writing--review and editing, L.G., H.Z. and L.F.; visualization, L.G.; supervision, L.F. All authors have read and agreed to the published version of the manuscript.  
This paper is supported in part by the National Natural Science Foundation of China (62371290, U20A20185) and 111 project (BP0719010).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors sincerely acknowledge Shanghai Jiao Tong University for supporting steps of this work.  
Author [PERSON] was employed by the company Huawei Technologies Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.