Fast and Adaptive Questionnaires for Voting Advice Applications
Fynn Bachmann, Cristina Sarasua, Abraham Bernstein

TL;DR
This paper presents an adaptive questionnaire system for Voting Advice Applications that improves recommendation accuracy and reduces question length by using a latent space model and question selection strategies, validated on Swiss election data.
Contribution
It introduces a novel adaptive questionnaire approach with encoder-decoder models and a question selector, enhancing accuracy over condensed questionnaires in VAAs.
Findings
Achieves 74% accuracy with fewer questions
Using IDEAL model improves prediction quality
PosteriorRMSE method optimizes question selection
Abstract
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,…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques
