Paolo Viappiani

1.1k total citations
33 papers, 459 citations indexed

About

Paolo Viappiani is a scholar working on Artificial Intelligence, Management Science and Operations Research and Information Systems. According to data from OpenAlex, Paolo Viappiani has authored 33 papers receiving a total of 459 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 14 papers in Management Science and Operations Research and 11 papers in Information Systems. Recurrent topics in Paolo Viappiani's work include Data Management and Algorithms (11 papers), Constraint Satisfaction and Optimization (8 papers) and Recommender Systems and Techniques (8 papers). Paolo Viappiani is often cited by papers focused on Data Management and Algorithms (11 papers), Constraint Satisfaction and Optimization (8 papers) and Recommender Systems and Techniques (8 papers). Paolo Viappiani collaborates with scholars based in Switzerland, Canada and France. Paolo Viappiani's co-authors include Pearl Pu, Craig Boutilier, Boi Faltings, Boi Faltings, Gabriella Pigozzi, Alexis Tsoukiàs, Patrice Perny, Marc Torrens, Neil Yorke‐Smith and Bart Peintner and has published in prestigious journals such as Journal of Nutrition, Artificial Intelligence and Lecture notes in computer science.

In The Last Decade

Paolo Viappiani

31 papers receiving 419 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Paolo Viappiani Switzerland 11 212 171 154 129 125 33 459
Marc Torrens Switzerland 11 159 0.8× 143 0.8× 36 0.2× 136 1.1× 166 1.3× 30 364
Adith Swaminathan United States 11 466 2.2× 382 2.2× 334 2.2× 81 0.6× 37 0.3× 22 756
Petros Venetis United States 8 314 1.5× 253 1.5× 188 1.2× 92 0.7× 104 0.8× 11 528
Or Sheffet United States 10 285 1.3× 36 0.2× 151 1.0× 39 0.3× 42 0.3× 25 498
Kristen Brent Venable Italy 19 496 2.3× 57 0.3× 323 2.1× 360 2.8× 262 2.1× 92 924
Saúl Vargas Spain 10 313 1.5× 717 4.2× 290 1.9× 85 0.7× 78 0.6× 19 866
Mukund Sundararajan United States 10 429 2.0× 70 0.4× 67 0.4× 89 0.7× 30 0.2× 13 539
Byron J. Gao United States 12 284 1.3× 198 1.2× 52 0.3× 96 0.7× 100 0.8× 46 479
Prasang Upadhyaya United States 11 244 1.2× 222 1.3× 136 0.9× 218 1.7× 35 0.3× 16 473
Florian Schoppmann United States 5 113 0.5× 88 0.5× 141 0.9× 120 0.9× 83 0.7× 6 351

Countries citing papers authored by Paolo Viappiani

Since Specialization
Citations

This map shows the geographic impact of Paolo Viappiani's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Paolo Viappiani with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paolo Viappiani more than expected).

Fields of papers citing papers by Paolo Viappiani

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paolo Viappiani. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Paolo Viappiani. The network helps show where Paolo Viappiani may publish in the future.

Co-authorship network of co-authors of Paolo Viappiani

This figure shows the co-authorship network connecting the top 25 collaborators of Paolo Viappiani. A scholar is included among the top collaborators of Paolo Viappiani based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Paolo Viappiani. Paolo Viappiani is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Viappiani, Paolo, et al.. (2024). Personalized bundle recommendation using preference elicitation and the Choquet integral. Frontiers in Artificial Intelligence. 7. 1346684–1346684. 1 indexed citations
2.
Viappiani, Paolo, Laurent Muller, C. Martin, et al.. (2022). Principles and Validations of an Artificial Intelligence-Based Recommender System Suggesting Acceptable Food Changes. Journal of Nutrition. 153(2). 598–604. 7 indexed citations
3.
Viappiani, Paolo & Craig Boutilier. (2020). On the equivalence of optimal recommendation sets and myopically optimal query sets. Artificial Intelligence. 286. 103328–103328. 7 indexed citations
4.
Viappiani, Paolo. (2019). Robust winner determination in positional scoring rules with uncertain weights. Theory and Decision. 88(3). 323–367. 3 indexed citations
5.
Perny, Patrice, et al.. (2017). Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems. Artificial Intelligence. 246. 152–180. 32 indexed citations
6.
Pigozzi, Gabriella, Alexis Tsoukiàs, & Paolo Viappiani. (2015). Preferences in artificial intelligence. Annals of Mathematics and Artificial Intelligence. 77(3-4). 361–401. 54 indexed citations
7.
Viappiani, Paolo, Sandra Zilles, Howard J. Hamilton, & Craig Boutilier. (2011). A Bayesian concept learning approach to crowdsourcing. VBN Forskningsportal (Aalborg Universitet). 756. 60–67. 1 indexed citations
8.
Viappiani, Paolo & Craig Boutilier. (2010). Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets. Neural Information Processing Systems. 23. 2352–2360. 46 indexed citations
9.
Boutilier, Craig, Kevin Regan, & Paolo Viappiani. (2010). Simultaneous Elicitation of Preference Features and Utility. Proceedings of the AAAI Conference on Artificial Intelligence. 24(1). 1160–1167. 6 indexed citations
10.
Viappiani, Paolo & Craig Boutilier. (2009). Optimal set recommendations based on regret. 20–31. 4 indexed citations
11.
Viappiani, Paolo & Craig Boutilier. (2009). Regret-based optimal recommendation sets in conversational recommender systems. 101–108. 31 indexed citations
12.
Viappiani, Paolo. (2007). Preference-based search with suggestions. Infoscience (Ecole Polytechnique Fédérale de Lausanne).
13.
Viappiani, Paolo, Boi Faltings, & Pearl Pu. (2006). The lookahead principle for preference elicitation: Experimental results. Lecture notes in computer science. 4027. 378–389. 3 indexed citations
14.
Viappiani, Paolo, Boi Faltings, & Pearl Pu. (2006). Evaluating preference-based search tools: a tale of two approaches. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 205–211. 15 indexed citations
15.
Viappiani, Paolo, Boi Faltings, & Pearl Pu. (2006). Preference-based Search using Example-Critiquing with Suggestions. Journal of Artificial Intelligence Research. 27. 465–503. 68 indexed citations
16.
Zhang, Jiyong, Pearl Pu, & Paolo Viappiani. (2006). A Study of User's Online Decision Making Behavior. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 2 indexed citations
17.
Viappiani, Paolo & Boi Faltings. (2006). Implementing example-based tools for preference-based search. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 89–89. 3 indexed citations
18.
Viappiani, Paolo, et al.. (2005). Stimulating preference expression using suggestions. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 128–133. 7 indexed citations
19.
Faltings, Boi, Pearl Pu, Marc Torrens, & Paolo Viappiani. (2004). Designing example-critiquing interaction. 8 indexed citations
20.
Faltings, Boi, Pearl Pu, Marc Torrens, & Paolo Viappiani. (2004). Designing example-critiquing interaction. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 22–29. 40 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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