Ai: To interpret or to explain?
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.
CV
Education
- Ph.D. in Computer Science, Université Paris Dauphine-PSL, Generality and explainability of recommender systems, November, 2023
- M.S. in Data Science, Université Paris Dauphine-PSL , September, 2019
- M.S. in Data Science, AgroParisTech, Ingénieur(M.S.), September, 2019
- B.S. in Engineering, Shanghai Jiao Tong University, June, 2017
Work experience
September 2025 -: Assistant professor (Maîtres de conférences) et University Paris 8 and the LIASD
- April 2024 - August 2025: Postdoctoral Researcher
- In collaboration with Pr. Emmanuel Bacry and Pr. Jean-françois muzy, we analyze and model the behaviors of the various agents involved in the same financial market as well as their interaction dynamics. To achieve this, we are fortunate to have access to an exceptionally rich database. This dataset is provided by Euronext (which owns the main market in the eurozone) and includes a complete historical record over several years of all orders placed in the order book of each of the 40 CAC40 stocks, as well as all orders placed in the order book of the futures contracts on the CAC40 index itself.
- October 2020 - April 2024: Temporary Lecturer and Research Assistant
- Université Paris Dauphine-PSL
- Courses taught: Algorithm et programming 3; VBA; Data base for actuarial; Machine learning for MIAGE
- April 2019 - October 2019: Research Intern
- Globéo Travel
- Duties included: Completion of bibliographic work; Selection of relevant variables to define customer and company segmentation; Development of models that provide personalized and proactive recommendations to clients; Writing a research project on the design and construction of a real-time recommendation system using a knowledge base.
- Supervisor: Corneliu Radulescu; Elsa NEGRE
- June 2018 - August 2018: Research Assistant
- AgroParisTech
- Duties included: Elaboration of a technical specifications document for the implementation of a platform for data management, storage, and statistical processing at the unit level.
Skills
- Python
- Deep learning frameworks: Pytorch, Tensorflow
- Data analysis, visualization: Pandas, Numpy, Seaborn, Matplotlib, Plotly…
- Machine Learning: Scikit-learn, Scipy…
- Database: SQL, Neo4J, Mongodb
- Git
- PHP, HTML, CSS
Publications
Context-aware explanations in recommender systems
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
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
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.
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
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
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?
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
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
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
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
Zhong, J. and Negre, E. (2025). Argumentation meets matrix factorization: A dual perspective for explainable recommendations. Applied Soft Computing
Talks
Vers l’amélioration des interactions entre utilisateurs et systèmes de recommandation
Talk at Workshop EXPLAIN'AI at EGC 2022, Blois, France
Amélioration des explications contrefactuelles pour les recommandations à l’aide de SHAP
Talk at EDA 2022 : 18ème journées Business Intelligence & Big Data, Clermont-Ferrand, France
$A^3R$: Explications argumentées pour les recommandations
Talk at Workshop ATELIER EXPLAIN'AI at EGC 2023, Lyon, France
Teaching
Service and leadership
- Reviewer of Explain’AI
- Reviewer of Data & Knowledge Engineering
- Reviewer of ADMA2023
- Volunteer of SUM 2022
- Organiser of Forum Horizon Chine 2019
- 2016-2017 President of Wine Association of SJTU