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.

翻译：投票建议应用（VAA）的有效性常受限于其问卷长度。为解决用户疲劳与不完整答复问题，部分应用（如瑞士Smartvote）提供了精简版问卷，但这些精简版无法确保候选党派或候选人推荐的准确性——本研究发现其准确率始终低于40%。为突破上述局限，本文提出一种自适应问卷方法，通过基于用户先前回答动态选择后续问题，旨在提升推荐准确率的同时减少需向选民提出的问题数量。本方法采用编码器-解码器模块，利用反映政治学传统可视化政治倾向方法的二维潜在空间，在任意完成阶段预测缺失值。此外，本文提出选择器模块，根据选民在潜在空间中的当前位置及剩余未答问题，确定最具信息量的后续问题。我们基于2019年瑞士联邦选举的Smartvote数据集验证该方法，测试了多种空间模型与选择策略以优化系统的预测准确性。研究结果表明：采用IDEAL模型同时作为编码器与解码器，结合PosteriorRMSE方法进行问题选择，在提问数量与精简版相同时即可将推荐准确率提升至74%。