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Voting Advice Applications (VAAs) aim to increase voters’ political competence by providing them with the closest political party according to their preferences. To do this, VAAs usually compare and aggregate the positions of users and political parties on a set of policy issues by defining a conceptual space and some distance metric on it. In this paper, we argue that the main method for performing the comparison adapts to users’ preferences unsatisfactorily because 1) they use unjustified  a priori  decisions for weighting policy issues and 2) they employ the same issue-voting space on all policy issues. Some exceptional cases address these issues by providing a community-based recommendation, but often come with lack of interpretability. To fill these gaps, we propose an adaptive algorithm that learns the configuration of the conceptual space from users’ answers. We employ a hybrid VAA that uses expert coding for the party positions and users’ data to adjust the calculation of the distance between users and parties. This new matching method, the Learning VAA, innovates by adjusting the saliency and issue-voting space for every policy issue. We argue and empirically demonstrate that our model fits better the users’ preferences while providing a higher degree of interpretability.

Learning VAA: A new method for matching users to parties in voting advice applications