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The master thesis consists of developing a framework for Group Recommender Systems and investigating the methods for generating recommendations to groups.

To date, traditional Deep Learning (DL) solutions (e.g. Feed-forward Neural Networks, Convolutional Neural Networks) have had a major impact in numerous fields, such as Speak Recognition (e.g., Siri, Alexa), Autonomous driving, Computer Vision,etc. It was just recently, however, that a new DL technique called Graph Neural Network (GNN) was introduced, proving to be unprecedentedly accurate to solve problems that are formalized as graphs.

Analyze the interplay between fairness and explainability in collaborative recommender systems.

Analyze the geographic imbalances in data to avoid disparities in the items recommended in collaborative filtering algorithms

Analyze interplay between privacy and fairness in collaborative recommender systems

Recent advances in the field of Reinforcement Learning (DRL) are rising a lot of attention due to its potential for automatic control and automatization. Breakthroughs from academia and the industry (e.g, Stanford, DeepMind and OpenAI) are demonstrating that DRL is an effective technique to face complex optimization problems with many dimensions and non-linearities. However, to train a DRL agent in large optimization scenarios still remains a challenge due to the computational intensive operations during backpropagation.

This project aims to analyze the prediction capability of Optical Coherence Tomography Angiography (OCTA) images for Diabetes Mellitus (DM) and Diabetic Retinopathy (DR,) in a large high-quality image dataset from previous research projects carried out in the field of Ophthalmology (Fundacio¿ La Marato¿ TV3, Fondo Investigaciones Sanitarias, FIS). OCTA is a newly developed, non-invasive, retinal imaging technique that permits adequate delineation of the perifoveal vascular network. It allows the detection of paramacular areas of capillary non perfusion and/or enlargement of the foveal avascular zone (FAZ), representing an excellent tool for assessment of DR.

The goal of this project is to analyze state-of-the-art metrics[1] that combines them to quantify the equality of learning opportunities among learners and mitigate the inequalities generated by the recommender systems as post-processed approach that balances personalization and learning opportunity equality in recommendations

The goal of this project is to analyze and mitigate bias in collaborative filtering recommender systems.

Recent advances in the field of Reinforcement Learning (DRL) are rising a lot of attention due to its potential for automatic control and automatization. Breakthroughs from academia and the industry (e.g, Stanford, DeepMind and OpenAI) are demonstrating that DRL is an effective technique to face complex optimization problems with many dimensions and non-linearities. However, to train a DRL agent in large optimization scenarios still remains a challenge due to the computational intensive operations during backpropagation.

Recent advances in the field of Reinforcement Learning (DRL) are rising a lot of attention due to its potential for automatic control and automatization. Breakthroughs from academia and the industry (e.g, Stanford, DeepMind and OpenAI) are demonstrating that DRL is an effective technique to face complex optimization problems with many dimensions and non-linearities. However, to train a DRL agent in large optimization scenarios still remains a challenge due to the computational intensive operations during backpropagation.

Check offers of other studies and specializations