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：现代社会投入大量时间进行数字化交互，日常行为多通过数字手段完成。这一趋势催生了众多人工智能工具，为生活的各个方面提供支持。推荐系统作为数字社会的关键工具，是一种智能系统——它通过分析用户历史行为数据，预测符合其兴趣偏好的新内容。部分推荐系统已发展为专门学习用户群体行为模式，为需要协同完成任务的群体提供推荐。本文分析了当前群组推荐系统的研究现状，并提出了两种基于新兴深度学习架构的新模型。实验结果表明，与现有最先进模型相比，所提模型在四个不同数据集上均取得了性能提升。模型源代码及所有实验代码均已公开在公共代码仓库中。