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This work was partially supported by Ministerio de Ciencia e Innovacion of Spain under the project PID2019-106493RB-I00 (DL-CEMG) and the Comunidad de Madrid under Convenio Plurianual with the Universidad Politecnica de Madrid in the actuation line of Programa de Excelencia para el Profesorado Universitario.

Analysis of institutional authors

Duenas-Lerin, JorgeAuthorLara-Cabrera, RaulCorresponding AuthorOrtega, FernandoAuthorBobadilla, JesusAuthor

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Article

Deep neural aggregation for recommending items to group of users

Publicated to:Applied Soft Computing. 175 113059- - 2025-05-01 175(), DOI: 10.1016/j.asoc.2025.113059

Authors: Duenas-Lerin, Jorge; Lara-Cabrera, Raul; Ortega, Fernando; Bobadilla, Jesus

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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.

Keywords

Collaborative filteringDeep learninDeep learningGroup recommender systems

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Applied Soft Computing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 27/197, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-09:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 2 (PlumX).

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (DUEÑAS LERÍN, JORGE) and Last Author (BOBADILLA SANCHO, JESUS).

the author responsible for correspondence tasks has been LARA CABRERA, RAUL.