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.

为了解决用户疲劳和不完整回答的问题，本研究提出了一种自适应问卷的方法，根据用户之前的回答选择后续问题，以提高推荐的准确性并减少问题的数量。通过使用编码器和解码器模块及选择器模块，结合瑞士联邦选举的Smartvote数据集进行验证，采用IDEAL模型作为编码器和解码器，并使用PosteriorRMSE方法进行问题选择，可显著提高推荐的准确性，实现与精简版问卷相同问题数量下的74%准确率。

快速且适应性强的投票建议问卷