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On the design of social voting recommendation applications


Tsapatsoulis, Nicolas; Agathokleous, Marilena; Djouvas, Constantinos; Mendez, Fernando (2015). On the design of social voting recommendation applications. International Journal on Artificial Intelligence Tools (IJAIT), 24(3):1550009.

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.

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.

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Additional indexing

Item Type:Journal Article, not_refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Political Science
Dewey Decimal Classification:320 Political science
Scopus Subject Areas:Physical Sciences > Artificial Intelligence
Uncontrolled Keywords:voting advice applications, design, machine learning, Mahalanobis classifier
Language:English
Date:June 2015
Deposited On:25 Oct 2021 15:34
Last Modified:25 Apr 2024 01:40
Publisher:World Scientific Publishing
ISSN:0218-2130
OA Status:Closed
Publisher DOI:https://doi.org/10.1142/s0218213015500098