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.

翻译：面向用户群体的推荐是推荐系统中的一个具有挑战性的子领域。其核心问题在于如何以及在哪里将每组用户信息聚合成一个独立实体，例如排序推荐列表、虚拟用户或多热输入向量编码。本文提出一种创新策略，即在输入神经网络的多元热向量中实现聚合。该聚合过程赋予概率语义，生成的输入向量可输入模型，使其能够从个体预测中便捷地泛化出群体推荐结果。此外，采用所提架构，仅需对预训练模型进行前馈传递个体评分即可获得群体推荐，无需获取包含用户群体信息的数据集，也无需分别进行个体与群体的两次训练。该方法还避免为支持个体与群体学习而维护两个不同模型。实验在三个代表性协同过滤数据集及一系列基线上测试了所提架构，结果表明，相比现有最优方法，该架构在准确率上取得了显著提升。