Call for Papers

The DA2PL workshop has been launched with the goal of bringing together researchers from operations research and the decision sciences with scholars from machine learning. It aims at providing a forum for discussing recent advances and identifying new research challenges in the intersection of both fields, which is marked by the growing field of preference learning, thereby supporting a cross-fertilisation of these disciplines.

Following the three previous editions of this workshop, which took place in Mons in 2012, Paris in 2014 and Paderborn in 2016, DA2PL'2018 will be held at the Poznan University of Technology, Poland, at November 22-23, 2018.

DA2PL'2018 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