On the Design of Social Voting Recommendation Applications

Abstract

Voting Advice Applications (VAAs) are online tools that match the policy preferences of voters with the policy positions of political parties or candidates. Designed to enhance the political competence of citizens, VAAs have become increasingly popular and institutionally embedded in a growing number of European countries. While the traditional VAA relied on the stated position or academically coded position of parties/candidates, a recent innovation has been to introduce a social vote recommendation borrowing the basic principles of collaborative filtering. The latter takes advantage of the community of VAA users to provide a vote recommendation. This paper provides an overview of the social vote recommendation scheme and tackles three problems related to its optimal implementation in a real–world setting: (1) the number of samples required to train party models; (2) whether this number is affected by differences in characteristics between early users versus late users; and (3) whether generalizations can be derived across VAA applications in different countries. For our experiments we use three real VAA datasets based on elections in Greece 2012, Cyprus 2013 and Germany 2013. The corresponding datasets are made freely available to other researchers working in the areas of VAA and web based recommender systems.