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数字交互。我们的日常行为多通过数字手段完成，这促使众多人工智能工具应运而生，为生活各方面提供助力。其中，推荐系统作为数字社会的关键工具，是一种通过学习用户历史行为以推荐符合其兴趣新内容的智能系统。部分推荐系统已专门研究如何通过分析用户群体行为，为有共同任务的群体提供建议。本文分析了群体推荐系统的当前研究现状，并提出两种采用新兴深度学习架构的新模型。实验结果表明，相较于现有最优模型，所提模型在四个不同数据集上均实现了性能提升。我们已将模型源代码及所有实验代码公开在公共仓库中。