Estimating the Recommendation Certainty in Candidate-Based Voting Advice Applications

Voting advice applications typically require users to answer questionnaires before receiving party or candidate recommendations. As users answer more questions, the recommendations naturally become more accurate. However, when users do not complete the questionnaire, the certainty of these recommendations is unknown. In this work, we develop and present a measure to quantify this certainty by introducing an algorithm that estimates the candidate recommendation accuracy—the overlap between early and final recommendations—after each question.