Designing example-critiquing interaction. - Boi Faltings; Pearl Pu; Marc Torrens; Paolo Viappiani (IUI, 2004)

Evaluating Preference-based Search Tools: A Tale of Two Approaches. - Paolo Viappiani; Boi Faltings; Pearl Pu (AAAI, 2006)

Increasing user decision accuracy using suggestions. - Pearl Pu; Paolo Viappiani; Boi Faltings (CHI, 2006)

The Lookahead Principle for Preference Elicitation: Experimental Results. - Paolo Viappiani; Boi Faltings; Pearl Pu (FQAS, 2006)

Implementing example-based tools for preference-based search. - Paolo Viappiani; Boi Faltings (ICWE, 2006)

Design and Implementation of Preference-Based Search. - Paolo Viappiani; Boi Faltings (WISE, 2006)

Preference-based Search using Example-Critiquing with Suggestions. - Paolo Viappiani; Boi Faltings; Pearl Pu (J. Artif. Intell. Res., 2006)

Conversational recommenders with adaptive suggestions. - Paolo Viappiani; Pearl Pu; Boi Faltings (RecSys, 2007)

Preference-based search with adaptive recommendations. - Paolo Viappiani; Pearl Pu; Boi Faltings (AI Commun., 2008)

Preferences in Interactive Systems: Technical Challenges and Case Studies. - Bart Peintner; Paolo Viappiani; Neil Yorke-Smith (AI Mag., 2008)

Online feature elicitation in interactive optimization. - Craig Boutilier; Kevin Regan 001; Paolo Viappiani (ICML, 2009)

Optimal Set Recommendations Based on Regret. - Paolo Viappiani; Craig Boutilier (ITWP, 2009)

Preference elicitation with subjective features. - Craig Boutilier; Kevin Regan 001; Paolo Viappiani (RecSys, 2009)

Regret-based optimal recommendation sets in conversational recommender systems. - Paolo Viappiani; Craig Boutilier (RecSys, 2009)

Simultaneous Elicitation of Preference Features and Utility. - Craig Boutilier; Kevin Regan 001; Paolo Viappiani (AAAI, 2010)

Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets. - Paolo Viappiani; Craig Boutilier (NIPS, 2010)

Recommendation Sets and Choice Queries: There Is No Exploration/Exploitation Tradeoff! - Paolo Viappiani; Craig Boutilier (AAAI, 2011)

A Bayesian Concept Learning Approach to Crowdsourcing. - Paolo Viappiani; Sandra Zilles; Howard J. Hamilton; Craig Boutilier (Interactive Decision Theory and Game Theory, 2011)

Learning Complex Concepts Using Crowdsourcing: A Bayesian Approach. - Paolo Viappiani; Sandra Zilles; Howard J. Hamilton; Craig Boutilier (ADT, 2011)

Usability Guidelines for Product Recommenders Based on Example Critiquing Research. - Pearl Pu; Boi Faltings; Li Chen 009; Jiyong Zhang; Paolo Viappiani (Recommender Systems Handbook, 2011)

Monte Carlo Methods for Preference Learning. - Paolo Viappiani (LION, 2012)

Thompson Sampling for Bayesian Bandits with Resets. - Paolo Viappiani (ADT, 2013)

Robust Optimization of Recommendation Sets with the Maximin Utility Criterion. - Paolo Viappiani; Christian Kroer (ADT, 2013)

Tutorial on preference handling. - Alexis Tsoukiàs; Paolo Viappiani (RecSys, 2013)

Preference Modeling and Preference Elicitation: An Overview. - Paolo Viappiani (DMRS, 2014)

Incremental Elicitation of Choquet Capacities for Multicriteria Decision Making. - Nawal Benabbou; Patrice Perny; Paolo Viappiani (ECAI, 2014)

Aggregation of Uncertain Qualitative Preferences for a Group of Agents. - Paolo Viappiani (IPMU (2), 2014)

Reducing the Number of Queries in Interactive Value Iteration. - Hugo Gilbert; Olivier Spanjaard; Paolo Viappiani; Paul Weng (ADT, 2015)

Solving MDPs with Skew Symmetric Bilinear Utility Functions. - Hugo Gilbert; Olivier Spanjaard; Paolo Viappiani; Paul Weng (IJCAI, 2015)

Characterization of Scoring Rules with Distances: Application to the Clustering of Rankings. - Paolo Viappiani (IJCAI, 2015)

Constructive Preference Elicitation by Setwise Max-Margin Learning. - Stefano Teso; Andrea Passerini; Paolo Viappiani (IJCAI, 2016)

Incremental Preference Elicitation in Multi-attribute Domains for Choice and Ranking with the Borda Count. - Nawal Benabbou; Serena Di Sabatino Di Diodoro; Patrice Perny; Paolo Viappiani (SUM, 2016)

Model-Free Reinforcement Learning with Skew-Symmetric Bilinear Utilities. - Hugo Gilbert; Bruno Zanuttini; Paul Weng; Paolo Viappiani; Esther Nicart (UAI, 2016)

Incremental Preference Elicitation for Decision Making Under Risk with the Rank-Dependent Utility Model. - Patrice Perny; Paolo Viappiani; Abdellah Boukhatem (UAI, 2016)

Preferences in artificial intelligence. - Gabriella Pigozzi; Alexis Tsoukiàs; Paolo Viappiani (Ann. Math. Artif. Intell., 2016)

Constructive Preference Elicitation for Multiple Users with Setwise Max-margin. - Stefano Teso; Andrea Passerini; Paolo Viappiani (ADT, 2017)

Incremental Decision Making Under Risk with the Weighted Expected Utility Model. - Hugo Gilbert; Nawal Benabbou; Patrice Perny; Olivier Spanjaard; Paolo Viappiani (IJCAI, 2017)

Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems. - Nawal Benabbou; Patrice Perny; Paolo Viappiani (Artif. Intell., 2017)

Positional Scoring Rules with Uncertain Weights. - Paolo Viappiani (SUM, 2018)

Interactive Elicitation of a Majority Rule Sorting Model with Maximum Margin Optimization. - Ons Nefla; Meltem Öztürk; Paolo Viappiani; Imène Brigui-Chtioui (ADT, 2019)

Simultaneous Elicitation of Committee and Voters' Preferences. - Beatrice Napolitano; Olivier Cailloux; Paolo Viappiani (RJCIA, 2019)

Incremental Elicitation of Capacities for the Sugeno Integral with a Maximin Approach. - Agnès Rico; Paolo Viappiani (SUM, 2020)

Agent-based ordinal classification for group decision making. - Ons Nefla; Imène Brigui; Meltem Öztürk; Paolo Viappiani; Oussama Raboun (WI/IAT, 2020)

On the equivalence of optimal recommendation sets and myopically optimal query sets. - Paolo Viappiani; Craig Boutilier (Artif. Intell., 2020)

Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination. - Beatrice Napolitano; Olivier Cailloux; Paolo Viappiani (ADT, 2021)

Preference Aggregation in the Generalised Unavailable Candidate Model. - Arnaud Grivet Sébert; Nicolas Maudet; Patrice Perny; Paolo Viappiani (ADT, 2021)

Rank Aggregation by Dissatisfaction Minimisation in the Unavailable Candidate Model. - Arnaud Grivet Sébert; Nicolas Maudet; Patrice Perny; Paolo Viappiani (AAMAS, 2021)

Efficient Exact Computation of Setwise Minimax Regret for Interactive Preference Elicitation. - Federico Toffano; Paolo Viappiani; Nic Wilson (AAMAS, 2021)

A Bayesian Interpretation of the Monty Hall Problem with Epistemic Uncertainty. - Cristina E. Manfredotti; Paolo Viappiani (MDAI, 2021)

Selecting the Most Relevant Elements from a Ranking over Sets. - Sébastien Konieczny; Stefano Moretti 001; Ariane Ravier; Paolo Viappiani (SUM, 2022)

Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences. - Giovanni De Toni; Paolo Viappiani; Bruno Lepri; Andrea Passerini (CoRR, 2022)

Environmentally-Aware Bundle Recommendation Using the Choquet Integral. - Marco Bronzini; Erich Robbi; Paolo Viappiani; Andrea Passerini (ECAI, 2023)

An Efficient Non-Bayesian Approach for Interactive Preference Elicitation Under Noisy Preference Models. - Samira Pourkhajouei; Federico Toffano; Paolo Viappiani; Nic Wilson (ECSQARU, 2023)

Modélisation du caractère séquentiel des repas pour améliorer la performance d'un système de recommandation alimentaire. - Noémie Jacquet; Vincent Guigue; Cristina E. Manfredotti; Fatiha Saïs; Stéphane Dervaux; Paolo Viappiani (EGC, 2024)

Social Ranking Under Incomplete Knowledge: Elicitation of the Lex-Cel Necessary Winners. - Ariane Ravier; Sébastien Konieczny; Stefano Moretti 001; Paolo Viappiani (SUM, 2024)

Personalized bundle recommendation using preference elicitation and the Choquet integral. - Erich Robbi; Marco Bronzini; Paolo Viappiani; Andrea Passerini (Frontiers Artif. Intell., 2024)

Personalized Algorithmic Recourse with Preference Elicitation. - Giovanni De Toni; Paolo Viappiani; Stefano Teso; Bruno Lepri; Andrea Passerini (Trans. Mach. Learn. Res., 2024)

Advances in preference handling: foreword. - Khaled Belahcène; Sébastien Destercke; Christophe Labreuche; Meltem Öztürk; Paolo Viappiani (Ann. Math. Artif. Intell., 2024)

NutriKG - un graphe de connaissances pour modéliser les préférences et les besoins nutritionnels. - Alexandre Combeau; Fatiha Saïs; Naggeta Kumari; Stéphane Dervaux; Cristina E. Manfredotti; Vincent Guigue; Paolo Viappiani (IC, 2025)

Fairness in Cooperative Multi-agent Multi-objective Reinforcement Learning using the Expected Scalarized Return. - Farès Chouaki; Aurélie Beynier; Nicolas Maudet; Paolo Viappiani (AAMAS, 2025)

Interactive preference elicitation under noisy preference models: An efficient non-Bayesian approach. - Guillaume Escamocher; Samira Pourkhajouei; Federico Toffano; Paolo Viappiani; Nic Wilson (Int. J. Approx. Reason., 2025)