DA2PL 2020

From Multiple Criteria Decision Aid to Preference Learning


5-6 November 2020
Trento - Italy

Department of Information Engineering and Computer Science (DISI)

DA2PL 2020 vs COVID-19: In order to guarantee the safety, health and well-being of the participants we have decided that DA2PL 2020 will take place entirely virtually. In order to maximise its interactiveness, the event will be organised as a live remote conference.


DA2PL 2020 (From Multiple Criteria Decision Aid to Preference Learning) aims to bring together researchers from decision analysis and machine learning. It provides a forum for discussing recent advances and identifying new research challenges in the intersection of both fields, thereby supporting a cross-fertilisation of these disciplines.

Following the four previous editions of this workshop, which took place in Mons in 2012, Paris in 2014, Paderborn in 2016 and Poznan in 2018, DA2PL 2020 will be held at the University of Trento, Trento - Italy.


Following the four previous editions of this workshop (DA2PL 2012, DA2PL 2014 and DA2PL 2016, DA2PL 2018), the fifth edition has the goal to bring together researchers from decision analysis and machine learning. It aims at providing a forum for discussing recent advances and identifying new research challenges in the intersection of both fields, thereby supporting a cross-fertilisation of these disciplines.

The notion of “preferences” has a long tradition in economics and operational research, where it has been formalised in various ways and studied extensively from different points of view. Nowadays, it is a topic of key importance in fields such as game theory, social choice and the decision sciences, including decision analysis and multicriteria decision aiding. In these fields, much emphasis is put in properly modelling a decision maker’s preferences, and on deriving and (axiomatically) characterizing rational decision rules.

In machine learning, like in artificial intelligence and computer science in general, the interest in the topic of preferences arose much more recently. The emerging field of preference learning is concerned with methods for learning preference models from explicit or implicit preference information, which are typically used for predicting the preferences of an individual or a group of individuals in new decision contexts. While research on preference learning has been specifically triggered by applications such as “learning to rank” for information retrieval (e.g., Internet search engines) and recommender systems, the methods developed in this field are useful in many other domains as well.

Preference modelling and decision analysis on the one side and preference learning on the other side can ideally complement and mutually benefit from each other. In particular, the suitable specification of an underlying model class is a key prerequisite for successful machine learning, that is to say, successful learning presumes appropriate modelling. Likewise, data-driven approaches for preference modelling and preference elicitation are becoming more and more important in decision analysis nowadays, mainly due to large scale applications, the proliferation of semi-automated computerised interfaces and the increasing availability of preference data.


DA2PL 2020 solicits contributions to the usage of theoretically supported preference models and formalisms in preference learning as well as communications devoted to innovative preference learning methods in decision analysis and multicriteria decision aiding. Specific topics of interest include, but are not limited to:

  • quantitative and qualitative approaches to modelling preferences, user feedback and training data

  • preference representation in terms of graphical models, logical formalisms, and soft constraints

  • dealing with incomplete and uncertain preferences

  • preference aggregation and disaggregation

  • learning utility functions using regression-based approaches

  • preference elicitation and active learning

  • preference learning in combinatorial domains

  • learning relational preference models and related regression problems

  • classification problems, such as ordinal and hierarchical classification

  • inducing monotonic decision models for preference representation

  • comparison of different preference learning paradigms (e.g., monolithic vs. decomposition)

  • ranking problems, such as object ranking, instance ranking and label ranking

  • complementarity of preference models and hybrid methods

  • explanation of recommendations

  • applications of preference learning, such as web search, information retrieval, electronic commerce, games, personalization, recommender systems


We solicit full research papers as well as extended abstracts reporting on more preliminary results. Submissions must be written in English and formatted according to the electronic template. Please download the LaTeX template and an example tex file.

The page limit is 6 for full papers and 2 for extended abstracts. As usual for DA2PL, submissions are NOT anonymous. According to the template, names of authors should be stated in the manuscript. All submissions will be reviewed by at least two referees. Submissions must be submitted through EasyChair.

Following a successful experience from the latest editions, DA2PL will provide a special opportunity for doctoral students to explore and develop their research interests. A special session at the conference will be devoted to PhD students, for presenting and discussing their ongoing research work. Therefore, we specifically encourage young researchers to submit their work (as full paper or extended abstract, depending on the maturity of the PhD project); the submission site will provide the option to mark a submission as a student paper.


Submission site opens: April 1, 2020
Paper submission: September 19, 2020
Author notification: October 19, 2020
Camera-ready version: October 31, 2020
Conference: November 5-6, 2020



General chair
Andrea Passerini, University of Trento

Program chairs
Vincent Mousseau, CentraleSupélec
Andrea Passerini, University of Trento

Organizing committee
Andrea Passerini, University of Trento
Giovanni Pellegrini, University of Trento


Program committee

Khaled Belahcene (Heudiasyc, UTC)
Denis Bouyssou (Université Paris Dauphine)
Róbert Busa-Fekete (Paderborn University)
Stéphan Clémençon (Telecom ParisTech)
Yves De Smet (Université Libre de Bruxelles)
Krzysztof Dembczynski (Poznan University of Technology)
Sébastien Destercke (CNRS, Heudiasyc)
Luis Dias (INESC Coimbra and Faculty of Economics, University of Coimbra)
Michael Doumpos (Technical University of Crete)
Paolo Dragone (Criteo)
Johanes Fürnkranz (TU Darmstadt)
Michel Grabisch (Univ. Paris I)
Salvatore Greco (University of Catania)
Eyke Hüllermeier (Paderborn University)
Ulrich Junker
Christophe Labreuche (Thales R&T)
Jérôme Lang (Lamsade - University Paris Dauphine)
Thibaut Lust (UPMC-LIP6)
Thierry Marchant (Universiteit Gent)
Brice Mayag (Lamsade - University Paris Dauphine)
Jérôme Mengin (IRIT - Université de Toulouse)
Patrick Meyer (IMT Atlantique)
Vincent Mousseau (MICS, CentraleSupélec)
Wassila Ouerdane (MICS, Ecole Centrale Paris)
Meltem Ozturk (Lamsade - University Paris Dauphine)
Andrea Passerini (Univ. Trento)
Patrice Perny (UPMC-LIP6)
Marc Pirlot (Université de Mons)
Antoine Rolland (Univ. Lyon 2)
Ahti Salo (Aalto Univ.)
Roman Slowinski (Poznan University of Technology)
Olivier Sobrie (Univ. Mons)
Bruno Teheux (University of Luxembourg)
Mikhail Timonin (Queen Mary University of London)
Alexis Tsoukias (Lamsade - University Paris Dauphine)
Aida Valls (University Rovira i Virgili)
Paolo Viappiani (CNRS and LIP6, Univ Pierre et Marie Curie)
Willem Waegeman (Ghent University)


Conference Office
Communication and Events Office – Polo Collina
University of Trento