Deep Neural Aggregation for Recommending Items to Group of Users

TL;DR

This paper introduces two novel deep learning models for group recommender systems, demonstrating improved performance over existing methods across multiple datasets, with publicly available source code.

Contribution

Proposes two new deep neural aggregation models specifically designed for group recommender systems, advancing the state-of-the-art in this field.

Findings

Improved recommendation accuracy over existing models

Validated on four different datasets

Source code publicly available

Abstract

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.