Modern society devotes a significant amount of time to digital interaction. Many of our daily actions are carried out through digital means. This has led to the emergence of numerous Artificial Intelligence tools that assist us in various aspects of our lives. One key tool for the digital society is Recommender Systems, intelligent systems that learn from our past actions to propose new ones that align with our interests. Some of these systems have specialized in learning from the behavior of user groups to make recommendations to a group of individuals who want to perform a joint task. In this article, we analyze the current state of Group Recommender Systems and propose two new models that use emerging Deep Learning architectures. Experimental results demonstrate the improvement achieved by employing the proposed models compared to the state-of-the-art models using four different datasets. The source code of the models, as well as that of all the experiments conducted, is available in a public repository.

翻译：现代社会将大量时间投入于数字化交互，我们日常行为中有诸多环节通过数字手段完成。这种趋势催生了众多人工智能工具，在生活各领域为人类提供辅助。推荐系统作为数字社会的关键工具之一，属于智能系统范畴——通过学习用户历史行为，提出符合其兴趣的新提议。部分推荐系统已专门研究如何从用户群体行为模式中学习，以向需要协作完成某项任务的群体提供推荐建议。本文在分析群组推荐系统研究现状的基础上，提出两种基于新兴深度学习架构的新模型。实验结果表明，与四种不同数据集上的现有最优模型相比，所提模型实现了性能提升。模型源代码及全部实验代码均已公开提供于公共仓库。