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To interpret or to explain?
Published:
Philosophers have delved into the nature, purpose, and structure of explanations, while cognitive and social psychologists have examined how individuals attribute and evaluate the behavior of others in physical environments. Additionally, cognitive psychologists and scientists have studied how people generate and evaluate explanations.
portfolio
Mont Saint Michel
Published:
Mont Saint Michel
publications
Ai: To interpret or to explain?
Published in Proceedings in INFORSID, 2021
Recommended citation: Zhong, J. and Negre, E. (2021). Ai: To interpret or to explain? In Congrès Inforsid (INFormatique des ORganisations et Systèmes d’Information et de Décision), pages 149 - 164.
Context-aware explanations in recommender systems
Published in Proceedings in ICDLAIR, 2021
Recommended citation: Zhong, J. and Negre, E. Context-aware explanations in recommender systems. In International Conference on Deep Learning, Artificial Intelligence and Robotics, pages 76–85. Springer.
Towards improving user-recommender systems interactions
Published in Proceedings in SII, 2022
Recommended citation: Zhong, J. and Negre, E. Towards improving user-recommender systems interactions. In 2022 IEEE/SICE International Symposium on System Integration (SII), pages 816–820. IEEE.
Towards better representation of context into recommender systems
Published in International Journal of Knowledge-Based Organizations (IJKBO), 2022
Recommended citation: Zhong, J. and Negre, E. Towards better representation of context into recommender systems. International Journal of Knowledge-Based Organizations (IJKBO), 12(2):1–12.
Shap-enhanced counterfactual explanations for recommendations.
Published in Proceedings SAC, 2022
Recommended citation: Zhong, J. and Negre, E. Shap-enhanced counterfactual explanations for recommendations. In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, pages 1365–1372.
$A^3R$: Argumentative explanations for recommendations
Published in Proceedings DSAA, 2022
Recommended citation: Zhong, J. and Negre, E. $A^3R$: Argumentative explanations for recommendations. In 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA), pages 1–9. IEEE.
Constraint-based recommender system for crisis management simulations
Published in Proceedings of HICSS, 2023
Recommended citation: Le Ngoc, L., Zhong, J., Negre, E., and Abel, M.-H. Constraint-based recommender system for crisis management simulations. In The 56th Hawaii International Conference on System Sciences, pages 1778–1789.
CORec-Cri: How collaborative and social technologies can help to contextualize crises?
Published in Proceedings of SMC, 2023
Recommended citation: Le Ngoc, L., Zhong, J., Negre, E., and Abel, M.-H. Corec-cri: How collaborative and social technologies can help to contextualize crises? To appear in SMC 2023.
Context-aware feature attribution through argumentation
Published in CARSs Workshop at Recsys, 2023
Recommended citation: Zhong, J. and Negre, E. (2023). Context-aware feature attribution through argumentation CARSs Workshop at Recsys 2023
Ontology-based crisis simulation system for population sheltering management
Published in Simulation: Transactions of the Society for Modeling and Simulation International, 2023
Recommended citation: Zhong, J., Le Ngoc, L., Negre, E., and Abel, M.-H. (2023). Ontology-based crisis simulation system for population sheltering management. SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL
Efficient and Effective Counterfactual Explanations for Random Forests
Published in Expert Systems with Applications, 2025
Recommended citation: Zhang, H. and Zhong, J. (2025). Efficient and Effective Counterfactual Explanations for Random Forests. Expert Systems with Applications
Argumentation meets matrix factorization: A dual perspective for explainable recommendations
Published in Applied Soft Computing, 2025
Recommended citation: Zhong, J. and Negre, E. (2025). Argumentation meets matrix factorization: A dual perspective for explainable recommendations. Applied Soft Computing
talks
teaching
Algorithmique et programmation 3 (2020-2023)
Undergraduate course (L2), Université Paris Dauphine-PSL, MIDO, 2020
Asymptotic comparison of algorithms: main complexity classes. Use of tree structures for search and sorting: binary trees and BSTs, balanced trees, heaps. Examples of advanced algorithms: integer and matrix multiplication, and exponentiation. Complexity theorem of recursive divide-and-conquer algorithms.
Base de données pour l’Actuariat (2020-2023)
Graduate Level (M2), Université Paris Dauphine-PS, MIDO, 2020
This course aims to enable students to understand the organization of data within a relational database and to know how to manipulate and manage this data. The course will also introduce the topic of Big Data, highlighting the challenges it poses, as well as the solutions and technologies available for managing large volumes of data.
Sciences du Digital, VBA
Undergraduate course (L2), Université Paris Dauphine-PSL, LSO, 2021
Outil Informaqique, Access
Undergraduate course (L1), Université Paris Dauphine-PSL, LSO, 2021
Projet
Machine Learning (2023-2024)
Postgraduate course (M2), Université Paris Dauphine-PSL, MIDO, 2023
Some basic and classical machine learning and deep learning algorithms: Decision Trees, Random Forests, SVM, SVD, NNs, CNN, Auto Encoders, etc. There will be a final project: constructing a recommender system using SVD.