In this article we review the existing techniques in group recommender systems and we propose some improvement based on the study of the different individual behaviors when carrying out a decision-making process. Our method includes an analysis of group personality composition and trust between each group member to improve the accuracy of group recommenders. This way we simulate the argumentation process followed by groups of people when agreeing on a common activity in a more realistic way. Moreover, we reflect how they expect the system to behave in a long term recommendation process. This is achieved by including a memory of past recommendations that increases the satisfaction of users whose preferences have not been taken into account in previous recommendations.

In this article we introduce a novel method of making recommendations to groups based on existing techniques of collaborative filtering and taking into account the group personality composition. We have tested our method in the movie recommendation domain and we have experimentally evaluated its behavior under heterogeneous groups according to the group personality composition.

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This paper presents a neural network-based classifier to predict whether a person is at risk of developing chronic kidney disease (CKD). The model is trained with the demographic data and medical care information of two population groups: on the one hand, people diagnosed with CKD in Colombia during 2018, and on the other, a sample of people without a diagnosis of this disease. Once the model is trained and evaluation metrics for classification algorithms are applied, the model achieves 95% accuracy in the test data set, making its application for disease prognosis feasible. However, despite the demonstrated efficiency of the neural networks to predict CKD, this machine-learning paradigm is opaque to the expert regarding the explanation of the outcome. Current research on eXplainable AI proposes the use of twin systems, where a black-box machine-learning method is complemented by another white-box method that provides explanations about the predicted values. Case-Based Reasoning (CBR) has proved to be an ideal complement as this paradigm is able to find explanatory cases for an explanation-by-example justification of a neural network's prediction. In this paper, we apply and validate a NN-CBR twin system for the explanation of CKD predictions. As a result of this research, 3,494,516 people were identified as being at risk of developing CKD in Colombia, or 7% of the total population. INDEX TERMS Chronic kidney disease prediction, neural networks, case-based reasoning, twin systems, explainable AI, support vector machines, random forest.

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