  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.
