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Journal Articles
- 2024
[j8]Erich Robbi, Marco Bronzini, Paolo Viappiani, Andrea Passerini:
Personalized bundle recommendation using preference elicitation and the Choquet integral. Frontiers Artif. Intell. 7 (2024)- [j7]Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini:
Personalized Algorithmic Recourse with Preference Elicitation. Trans. Mach. Learn. Res. 2024 (2024) - 2020
- [j6]Paolo Viappiani
, Craig Boutilier:
On the equivalence of optimal recommendation sets and myopically optimal query sets. Artif. Intell. 286: 103328 (2020) - 2017
- [j5]Nawal Benabbou, Patrice Perny, Paolo Viappiani:
Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems. Artif. Intell. 246: 152-180 (2017) - 2016
- [j4]Gabriella Pigozzi, Alexis Tsoukiàs, Paolo Viappiani:
Preferences in artificial intelligence. Ann. Math. Artif. Intell. 77(3-4): 361-401 (2016) - 2008
- [j3]Paolo Viappiani, Pearl Pu, Boi Faltings:
Preference-based search with adaptive recommendations. AI Commun. 21(2-3): 155-175 (2008) - [j2]Bart Peintner, Paolo Viappiani, Neil Yorke-Smith:
Preferences in Interactive Systems: Technical Challenges and Case Studies. AI Mag. 29(4): 13-24 (2008) - 2006
- [j1]Paolo Viappiani, Boi Faltings, Pearl Pu:
Preference-based Search using Example-Critiquing with Suggestions. J. Artif. Intell. Res. 27: 465-503 (2006)
Conference and Workshop Papers
- 2024
- [c48]Noémie Jacquet, Vincent Guigue, Cristina E. Manfredotti, Fatiha Saïs, Stéphane Dervaux, Paolo Viappiani:
Modélisation du caractère séquentiel des repas pour améliorer la performance d'un système de recommandation alimentaire. EGC 2024: 131-142 - [c47]Ariane Ravier, Sébastien Konieczny, Stefano Moretti, Paolo Viappiani:
Social Ranking Under Incomplete Knowledge: Elicitation of the Lex-Cel Necessary Winners. SUM 2024: 378-393 - 2023
- [c46]Marco Bronzini, Erich Robbi, Paolo Viappiani, Andrea Passerini:
Environmentally-Aware Bundle Recommendation Using the Choquet Integral. ECAI 2023: 3182-3189 - [c45]Samira Pourkhajouei, Federico Toffano, Paolo Viappiani, Nic Wilson:
An Efficient Non-Bayesian Approach for Interactive Preference Elicitation Under Noisy Preference Models. ECSQARU 2023: 308-321 - 2022
- [c44]Sébastien Konieczny, Stefano Moretti, Ariane Ravier, Paolo Viappiani:
Selecting the Most Relevant Elements from a Ranking over Sets. SUM 2022: 172-185 - 2021
- [c43]Arnaud Grivet Sébert, Nicolas Maudet, Patrice Perny, Paolo Viappiani:
Preference Aggregation in the Generalised Unavailable Candidate Model. ADT 2021: 35-50 - [c42]Beatrice Napolitano, Olivier Cailloux, Paolo Viappiani:
Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination. ADT 2021: 51-67 - [c41]Federico Toffano, Paolo Viappiani, Nic Wilson:
Efficient Exact Computation of Setwise Minimax Regret for Interactive Preference Elicitation. AAMAS 2021: 1326-1334 - [c40]Arnaud Grivet Sébert, Nicolas Maudet, Patrice Perny, Paolo Viappiani:
Rank Aggregation by Dissatisfaction Minimisation in the Unavailable Candidate Model. AAMAS 2021: 1518-1520 - [c39]Cristina E. Manfredotti, Paolo Viappiani:
A Bayesian Interpretation of the Monty Hall Problem with Epistemic Uncertainty. MDAI 2021: 93-105 - 2020
- [c38]Agnès Rico, Paolo Viappiani:
Incremental Elicitation of Capacities for the Sugeno Integral with a Maximin Approach. SUM 2020: 156-171 - [c37]Ons Nefla, Imène Brigui, Meltem Öztürk, Paolo Viappiani, Oussama Raboun:
Agent-based ordinal classification for group decision making. WI/IAT 2020: 365-370 - 2019
- [c36]Ons Nefla, Meltem Öztürk, Paolo Viappiani, Imène Brigui-Chtioui:
Interactive Elicitation of a Majority Rule Sorting Model with Maximum Margin Optimization. ADT 2019: 141-157 - [c35]Beatrice Napolitano, Olivier Cailloux, Paolo Viappiani:
Simultaneous Elicitation of Committee and Voters' Preferences. RJCIA 2019: 59-62 - 2018
- [c34]Paolo Viappiani:
Positional Scoring Rules with Uncertain Weights. SUM 2018: 306-320 - 2017
- [c33]Stefano Teso, Andrea Passerini, Paolo Viappiani:
Constructive Preference Elicitation for Multiple Users with Setwise Max-margin. ADT 2017: 3-17 - [c32]Hugo Gilbert, Nawal Benabbou, Patrice Perny, Olivier Spanjaard, Paolo Viappiani:
Incremental Decision Making Under Risk with the Weighted Expected Utility Model. IJCAI 2017: 4588-4594 - 2016
- [c31]Stefano Teso, Andrea Passerini, Paolo Viappiani:
Constructive Preference Elicitation by Setwise Max-Margin Learning. IJCAI 2016: 2067-2073 - [c30]Nawal Benabbou, Serena Di Sabatino Di Diodoro, Patrice Perny, Paolo Viappiani:
Incremental Preference Elicitation in Multi-attribute Domains for Choice and Ranking with the Borda Count. SUM 2016: 81-95 - [c29]Hugo Gilbert, Bruno Zanuttini, Paul Weng, Paolo Viappiani, Esther Nicart:
Model-Free Reinforcement Learning with Skew-Symmetric Bilinear Utilities. UAI 2016 - [c28]Patrice Perny, Paolo Viappiani, Abdellah Boukhatem:
Incremental Preference Elicitation for Decision Making Under Risk with the Rank-Dependent Utility Model. UAI 2016 - 2015
- [c27]Hugo Gilbert, Olivier Spanjaard, Paolo Viappiani, Paul Weng:
Reducing the Number of Queries in Interactive Value Iteration. ADT 2015: 139-152 - [c26]Paolo Viappiani:
Characterization of Scoring Rules with Distances: Application to the Clustering of Rankings. IJCAI 2015: 104-110 - [c25]Hugo Gilbert, Olivier Spanjaard, Paolo Viappiani, Paul Weng:
Solving MDPs with Skew Symmetric Bilinear Utility Functions. IJCAI 2015: 1989-1995 - 2014
- [c24]Paolo Viappiani:
Preference Modeling and Preference Elicitation: An Overview. DMRS 2014: 19-24 - [c23]Nawal Benabbou, Patrice Perny, Paolo Viappiani:
Incremental Elicitation of Choquet Capacities for Multicriteria Decision Making. ECAI 2014: 87-92 - [c22]Paolo Viappiani:
Aggregation of Uncertain Qualitative Preferences for a Group of Agents. IPMU (2) 2014: 434-443 - 2013
- [c21]Paolo Viappiani:
Thompson Sampling for Bayesian Bandits with Resets. ADT 2013: 399-410 - [c20]Paolo Viappiani, Christian Kroer:
Robust Optimization of Recommendation Sets with the Maximin Utility Criterion. ADT 2013: 411-424 - [c19]Alexis Tsoukiàs, Paolo Viappiani:
Tutorial on preference handling. RecSys 2013: 497-498 - 2012
- [c18]Paolo Viappiani:
Monte Carlo Methods for Preference Learning. LION 2012: 503-508 - 2011
- [c17]Paolo Viappiani, Craig Boutilier:
Recommendation Sets and Choice Queries: There Is No Exploration/Exploitation Tradeoff! AAAI 2011: 1571-1574 - [c16]Paolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier:
A Bayesian Concept Learning Approach to Crowdsourcing. Interactive Decision Theory and Game Theory 2011 - [c15]Paolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier:
Learning Complex Concepts Using Crowdsourcing: A Bayesian Approach. ADT 2011: 277-291 - [c14]Paolo Viappiani, Sandra Zilles, Howard J. Hamilton, Craig Boutilier:
A Bayesian Concept Learning Approach to Crowdsourcing. ITWP@IJCAI 2011 - 2010
- [c13]Craig Boutilier, Kevin Regan, Paolo Viappiani:
Simultaneous Elicitation of Preference Features and Utility. AAAI 2010: 1160-1167 - [c12]Paolo Viappiani, Craig Boutilier:
Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets. NIPS 2010: 2352-2360 - 2009
- [c11]Craig Boutilier, Kevin Regan, Paolo Viappiani:
Online feature elicitation in interactive optimization. ICML 2009: 73-80 - [c10]Paolo Viappiani, Craig Boutilier:
Optimal Set Recommendations Based on Regret. ITWP 2009 - [c9]Paolo Viappiani, Craig Boutilier:
Regret-based optimal recommendation sets in conversational recommender systems. RecSys 2009: 101-108 - [c8]Craig Boutilier, Kevin Regan, Paolo Viappiani:
Preference elicitation with subjective features. RecSys 2009: 341-344 - 2007
- [c7]Paolo Viappiani, Pearl Pu, Boi Faltings:
Conversational recommenders with adaptive suggestions. RecSys 2007: 89-96 - 2006
- [c6]Paolo Viappiani, Boi Faltings, Pearl Pu:
Evaluating Preference-based Search Tools: A Tale of Two Approaches. AAAI 2006: 205-212 - [c5]Pearl Pu, Paolo Viappiani, Boi Faltings:
Increasing user decision accuracy using suggestions. CHI 2006: 121-130 - [c4]Paolo Viappiani, Boi Faltings, Pearl Pu:
The Lookahead Principle for Preference Elicitation: Experimental Results. FQAS 2006: 378-389 - [c3]Paolo Viappiani, Boi Faltings:
Implementing example-based tools for preference-based search. ICWE 2006: 89-90 - [c2]Paolo Viappiani, Boi Faltings:
Design and Implementation of Preference-Based Search. WISE 2006: 72-83 - 2004
- [c1]Boi Faltings, Pearl Pu, Marc Torrens, Paolo Viappiani:
Designing example-critiquing interaction. IUI 2004: 22-29
Parts in Books or Collections
- 2011
- [p1]Pearl Pu, Boi Faltings, Li Chen, Jiyong Zhang, Paolo Viappiani:
Usability Guidelines for Product Recommenders Based on Example Critiquing Research. Recommender Systems Handbook 2011: 511-545
Editorship
- 2015
- [e2]Dietmar Jannach, Jérôme Mengin, Bamshad Mobasher, Andrea Passerini, Paolo Viappiani:
Proceedings of the IJCAI 2015 Joint Workshop on Constraints and Preferences for Configuration and Recommendation and Intelligent Techniques for Web Personalization co-located with the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 27, 2015. CEUR Workshop Proceedings 1440, CEUR-WS.org 2015 [contents] - 2013
- [e1]Manfred Jaeger, Thomas Dyhre Nielsen, Paolo Viappiani:
Twelfth Scandinavian Conference on Artificial Intelligence, SCAI 2013, Aalborg, Denmark, November 20-22, 2013. Frontiers in Artificial Intelligence and Applications 257, IOS Press 2013, ISBN 978-1-61499-329-2 [contents]
Informal and Other Publications
- 2022
- [i3]Giovanni De Toni, Paolo Viappiani, Bruno Lepri, Andrea Passerini:
Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences. CoRR abs/2205.13743 (2022) - 2016
- [i2]Stefano Teso, Andrea Passerini, Paolo Viappiani:
Constructive Preference Elicitation by Setwise Max-margin Learning. CoRR abs/1604.06020 (2016) - 2011
- [i1]Boi Faltings, Pearl Pu, Paolo Viappiani:
Preference-based Search using Example-Critiquing with Suggestions. CoRR abs/1110.0026 (2011)
Coauthor Index
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last updated on 2024-11-27 21:23 CET by the dblp team
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