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.
Aims and Topics
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.
Call for papers
DA2PL2020 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. The LaTeX template and an example tex file are available in the download box. 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. Submissions must be submitted through EasyChair and will be reviewed by at least two referees.
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: August 22, 2020
Author notification: September 20, 2020
Camera-ready version: October 21, 2020
Conference: November 5-6, 2020
Andrea Passerini, University of Trento
Vincent Mousseau, CentraleSupélec
Andrea Passerini, University of Trento
For scientific matters:andrea.passerini [at] unitn.it ( Andrea Passerini )
For organizing matters: comunicazione-collina [at] unitn.it (Communication and Events Office)
edited by Communication and Events Office - polo di Collina