Not logged in.

Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Fast and Adaptive Questionnaires for Voting Advice Applications
Organization Unit
Authors
  • Fynn Bachmann
  • Cristina Sarasua
  • Abraham Bernstein
Presentation Type paper
Item Subtype Original Work
Refereed No
Status Published in final form
Language
  • English
ISBN 978-3-031-70381-2
ISSN 2945-9133
Page Range 365 - 380
Event Title Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track
Event Type conference
Event Location Vilnius, Lithuania
Event Start Date September 9 - 2024
Event End Date September 13 - 2024
Series Name Lecture Notes in Artificial Intelligence
Number 14950
Place of Publication Cham
Publisher Springer
Abstract Text The effectiveness of Voting Advice Applications (VAA) is often compromised by the length of their questionnaires. To address user fatigue and incomplete responses, some applications (such as the Swiss Smartvote) offer a condensed version of their questionnaire. However, these condensed versions can not ensure the accuracy of recommended parties or candidates, which we show to remain below 40%. To tackle these limitations, this work introduces an adaptive questionnaire approach that selects subsequent questions based on users’ previous answers, aiming to enhance recommendation accuracy while reducing the number of questions posed to the voters. Our method uses an encoder and decoder module to predict missing values at any completion stage, leveraging a two-dimensional latent space reflective of political science’s traditional methods for visualizing political orientations. Additionally, a selector module is proposed to determine the most informative subsequent question based on the voter’s current position in the latent space and the remaining unanswered questions. We validated our approach using the Smartvote dataset from the Swiss Federal elections in 2019, testing various spatial models and selection methods to optimize the system’s predictive accuracy. Our findings indicate that employing the IDEAL model both as encoder and decoder, combined with a PosteriorRMSE method for question selection, significantly improves the accuracy of recommendations, achieving 74% accuracy after asking the same number of questions as in the condensed version.
Digital Object Identifier 10.1007/978-3-031-70381-2_23
PDF File Download from ZORA
Export BibTeX
EP3 XML (ZORA)