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paolo.viappiani@lamsade.dauphine.fr
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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.
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
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
De Toni G., Viappiani P., Teso S., Lepri B., Passerini A. (2024), Personalized Algorithmic Recourse with Preference Elicitation, Transactions on machine learning research
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
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.
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.
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 Rădulescu, Amsterdam, IOS Press, 3182-3189 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.
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
