Daniel Xavier de Sousa

2025

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Risk-sensitive optimization of neural deep learning ranking models with applications in ad-hoc retrieval and recommender systems.
Pedro Henrique Silva Rodrigues|Daniel Xavier de Sousa|Celso França|Gestefane Rabbi|Thierson Couto Rosa|Marcos André Gonçalves
Information Processing and Management 2025 Volume 62 Issue 5
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A Robustness Assessment of Query Performance Prediction (QPP) Methods Based on Risk-Sensitive Analysis.
Ricardo Marçal de Andrade Nascimento|Daniel Xavier de Sousa|Guglielmo Faggioli|Paulo José Lage Alvarenga|Nicola Ferro|Marcos André Gonçalves
Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025

2022

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Risk-Sensitive Deep Neural Learning to Rank.
Pedro Henrique Silva Rodrigues|Daniel Xavier de Sousa|Thierson Couto Rosa|Marcos André Gonçalves
SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022

2019

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Risk-Sensitive Learning to Rank with Evolutionary Multi-Objective Feature Selection.
Daniel Xavier de Sousa|Sérgio D. Canuto|Marcos André Gonçalves|Thierson Couto Rosa|Wellington Santos Martins
ACM Transactions on Information Systems (TOIS) 2019 Volume 37 Issue 2

2016

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Incorporating Risk-Sensitiveness into Feature Selection for Learning to Rank.
Daniel Xavier de Sousa|Sérgio Daniel Canuto|Thierson Couto Rosa|Wellington Santos Martins|Marcos André Gonçalves
Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24-28, 2016
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Generalized BROOF-L2R: A General Framework for Learning to Rank Based on Boosting and Random Forests.
Clebson C. A. de Sá|Marcos André Gonçalves|Daniel Xavier de Sousa|Thiago Salles
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR 2016, Pisa, Italy, July 17-21, 2016