• Submissions and dates

    DA2PL accepts two kinds of submissions:

    • Long papers that will undergo a full review process, should be at most 8 pages long in a 2‑columns format and submitted before the paper submission deadline. 
    • Extended abstracts that should be at most 2 pages long, will undergo a light review process and are intended to present preliminary works that would not justify a full paper submission. Abstracts will be reviewed on the fly and can be submitted up to the deadline for giving Camera-ready version of papers.

    While writing your paper or abstract, you should follow the template given here.

    Your paper should be submitted on the easychair platform, to which you can connect by clicking here.

    We are currently planning on the following schedule:

    • May 1st: submissions open
    • September 1st 15th (final deadline): paper submission deadline
    • September 30th October 10th: author notification
    • November 3rd: Camera-ready version of papers
    • November 1718: Conference

    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, …