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