  Recommendation to groups of users is a challenging subfield of recommendation
systems. Its key concept is how and where to make the aggregation of each set
of user information into an individual entity, such as a ranked recommendation
list, a virtual user, or a multi-hot input vector encoding. This paper proposes
an innovative strategy where aggregation is made in the multi-hot vector that
feeds the neural network model. The aggregation provides a probabilistic
semantic, and the resulting input vectors feed a model that is able to
conveniently generalize the group recommendation from the individual
predictions. Furthermore, using the proposed architecture, group
recommendations can be obtained by simply feedforwarding the pre-trained model
with individual ratings; that is, without the need to obtain datasets
containing group of user information, and without the need of running two
separate trainings (individual and group). This approach also avoids
maintaining two different models to support both individual and group learning.
Experiments have tested the proposed architecture using three representative
collaborative filtering datasets and a series of baselines; results show
suitable accuracy improvements compared to the state-of-the-art.
