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Paolo Viappiani is a CNRS researcher since 2012, affiliated with LIP6 (Sorbonne Université) until September 2021, and since then affiliated with LAMSADE (Université Paris Dauphine). He holds an engineering diploma from Politecnico di Milano and a PhD in Computer Science from EPFL.
His research interests span algorithmic decision theory, artificial intelligence, recommender systems.
Escamocher G., Pourkhajouei S., Toffano F., Viappiani P., Wilson N. (2025), Interactive preference elicitation under noisy preference models: An efficient non-Bayesian approach, International Journal of Approximate Reasoning, vol. 178, p. 109333 
Belahcène K., Destercke S., Labreuche C., Ozturk M., Viappiani P. (2024), Advances in preference handling: foreword, Annals of Mathematics and Artificial Intelligence, vol. 92, n°6, p. 1377-1379
Viappiani P. (2024), Volumetric Aggregation Methods for Scoring Rules with Unknown Weights, Group Decision and Negotiation, vol. 33, p. 515–563
De Toni G., Viappiani P., Teso S., Lepri B., Passerini A. (2024), Personalized Algorithmic Recourse with Preference Elicitation, Transactions on machine learning research
Robbi E., Bronzini M., Viappiani P., Passerini A. (2024), Personalized bundle recommendation using preference elicitation and the Choquet integral, Frontiers in Artificial Intelligence, vol. 7
Vandeputte J., Herold P., Kuslii M., Viappiani P., Muller L., Martin C., Davidenko O., Delaere F., Manfredotti C., Cornuéjols A., Darcel N. (2023), Principles and Validations of an Artificial Intelligence-Based Recommender System Suggesting Acceptable Food Changes, The Journal of Nutrition, vol. 153, n°2, p. 598-604
Pigozzi G., Tsoukias A., Viappiani P. (2016), Preferences in Artificial Intelligence, Annals of Mathematics and Artificial Intelligence, vol. 77, n°3, p. 361-401
Fares Chouaki F., Beynier A., Maudet N., Viappiani P. (2025), Fairness in Cooperative Multi-agent Multi-objective Reinforcement Learning using the Expected Scalarized Return, in , New York, NY, ACM - Association for Computing Machinery, 2469 - 2471 p.
Combeau A., Saïs F., Kumari N., Dervaux S., Manfredotti C., Guigue V., Viappiani P. (2025), NutriKG -Un Graphe de Connaissances pour Modéliser les Préférences et les Besoins Nutritionnels, in , Association française pour l'Intelligence Artificielle (AFIA) , 8-17 p.
Ravier A., Konieczny S., Moretti S., Viappiani P. (2025), From Order Lifting to Social Ranking: Recovering Preferences from Partial Extensions, in Inês Lynce, Nello Murano, Mauro Vallati, Serena Villata, Federico Chesani, Michela Milano, Andrea Omicini, Mehdi Dastani, ECAI 2025, Amsterdam, IOS Press, 723 - 730 p.
Konieczny S., Moretti S., Ravier A., Viappiani P. (2024), Social Ranking under Incomplete Knowledge: Elicitation of the Lex-cel Necessary Winners, in Sébastien Destercke, Maria Vanina Martinez, Giuseppe Sanfilippo, Springer, 378-393 p.
Jacquet N., Guigue V., Manfredotti C., Saïs F., Dervaux S., Viappiani P. (2024), Modélisation du caractère séquentiel des repas pour améliorer la performance d'un système de recommandation alimentaire, in Jérôme Gensel ; Christophe Cruz ; Hocine Cherif, Editions RNTI, 131-142 p.
Bronzini M., Robbi E., Viappiani P., Passerini A. (2023), Environmentally-Aware Bundle Recommendation Using the Choquet Integral, in Kobi Gal . Ann Nowé ; Grzegorz J. Nalepa ; Roy Fairstein ; Roxana R?dulescu, Amsterdam, IOS Press, 3182-3189 p.
Pourkhajouei S., Toffano F., Viappiani P., Wilson N. (2023), An Efficient Non-Bayesian Approach for Interactive Preference Elicitation Under Noisy Preference Models, in Zied Bouraoui ; Srdjan Vesic, Berlin Heidelberg, Springer International Publishing, 308-321 p.
Konieczny S., Moretti S., Ravier A., Viappiani P. (2022), Selecting the Most Relevant Elements from a Ranking over Sets, in Florence Dupin de Saint-Cyr, Meltem Öztürk-Escoffier, Nico Potyka, Springer, 172-185 p.
Napolitano B., Cailloux O., Viappiani P. (2021), Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination, in Dimitris Fotakis, David Ríos Insua, Springer, 51-67 p.
Napolitano B., Cailloux O., Viappiani P., Willott C., Delorme P., Reylé C. (2019), Simultaneous Elicitation of Committee and Voters' Preferences, in Maxime Lefrançois, Actes des 17èmes Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA 2019), Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 59-62 p.
Nefla O., Ozturk M., Viappiani P., Brigui I. (2019), Interactive Elicitation for a Majority Sorting Model with Maximum Margin optimization, in Saša Peke?, Kristen Brent Venable, Algorithmic Decision Theory - 6th International Conference (ADT 2019), Springer, 141-157 p.
Tsoukiàs A., Viappiani P. (2013), Preference Handling (Tutorial on), in Qiang Yang, Irwin King, Qing Li, RecSys '13 Proceedings of the 7th ACM conference on Recommender systems, Hong Kong, Association Française de Marketing, 497-498 p.
Nefla O., Brigui I., Viappiani P., Raboun O. (2020), Agent-based ordinal classification for group decision making, The 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT'20), Melbourne (en virtuel), Australie
Nefla O., Ozturk M., Viappiani P., Brigui-Chtioui I., Raboun O. (2020), Group-based Ordinal Classification based on a Negotiation Process, Rencontre des Jeunes Chercheur·ses en Intelligence Artificielle, Angers, France
Jacquet N., Manfredotti C., Guigue V., Saïs F., Viappiani P. (2023), An EXplainable RecommandER SYStem for the Nutrition Domain, combining Knowledge Graphs and Machine Learning, Paris, Preprint Lamsade
