Work

Deep neural aggregation for recommending items to group of users

Year: 2025

Type: article

Abstract: Modern society dedicates a significant amount of time to digital interaction, as social life is more and more related to digital life, the information of groups’ interaction with the elements of the system is increasing. 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. This research presents an innovative approach to representing group user preferences using deep learning techniques, enhancing recommendations for joint tasks. The proposed aggregation model has been evaluated using two different foundational models, GMF and MLP, four different datasets, and nine group sizes. The experimental results demonstrate the improvement achieved by employing the proposed aggregation model compared to the state-of-the-art, and this aggregation strategy can be applied to upcoming models and architectures. • DL based CF provides accurate predictions for both individual users and user groups. • Learning group aggregation strategy from data increases prediction performances. • Larger datasets require that GRS be based on bigger NN. • The quality of the GRS predictions is not related to the size of the group.

Source: Applied Soft Computing

DOI: 10.1016/j.asoc.2025.113059

Authors: Jorge Dueñas-Lerín, Raúl Lara-Cabrera, Fernando Ortega, Jesús Bobadilla

Institution: Universidad Politécnica de Madrid

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,730

Funders: Comunidad de Madrid, Universidad Politécnica de Madrid, Ministerio de Ciencia e Innovación