Publications

Fast and Adaptive Questionnaires for Voting Advice Applications

This research introduces an adaptive questionnaire for Voting Advice Applications that enhances recommendation accuracy from 40% to 74% by dynamically selecting questions based on users’ previous answers.

Authors: Fynn Bachmann, Cristina Sarasua, and Abraham Bernstein
Published in: Proceedings of the European Conference for Machine Learning (ECML) 2024
Link to publication: Fast and Adaptive Questionnaires for Voting Advice Applications