On the Quantification of Missing Value Impact on Voting Advice Applications

  • Marilena Agathokleous
  • Nicolas Tsapatsoulis
  • Ioannis Katakis
Part of the Communications in Computer and Information Science book series (CCIS, volume 383)


Voting Advice Application (VAA) is a web application that recommends a candidate or a party to a voter. From an online questionnaire, which voters and candidates are called to answer, the VAA proposes to each individual voter the candidate who replied like him/her. It is very important the voters to reply in all questions of the questionnaire, because every question has its meaning and is responding to the political position of a each party. Missing values might mislead the VAA and impede it to have complete knowledge about the voter, as a result to offer him/her the wrong candidate. In this paper we quantitatively investigate the effect of missing values in VAAs by examining the impact of the number of missing values to different methods of voting prediction. For our experiment we have used the data obtained from the May parliamentary elections in Greece in 2012. The corresponding dataset is made freely available to other researchers working in the areas of VAA and recommender systems through the Web.


Missing values classifiers recommender systems voting advice applications 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baka, A., Figgou, L., Triga, V.: ‘Neither agree, nor disagree’: a critical analysis of the middle answer category in Voting Advice Applications. Int. J. Electronic Governance 5(3/4), 244–263 (2012)CrossRefGoogle Scholar
  2. 2.
    Cedroni, L., Diego, G. (eds.): Voting Advice Applications in Europe: The State of the Art. ScriptaWeb, Napoli (2010)Google Scholar
  3. 3.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  4. 4.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann (2011)Google Scholar
  5. 5.
    Katakis, I., Tsapatsoulis, N., Triga, V., Tziouvas, C., Mendez, F.: Clustering Online Poll Data: Towards a Voting Assistance System. In: Semantic and Social Media Adaptation and Personalization (SMAP 2012), pp. 54–59. IEEE Press (2012)Google Scholar
  6. 6.
    Ladner, A., Pianzola, J.: Do Voting Advice Applications Have an Effect on Electoral Participation and Voter Turnout? Evidence from the 2007 Swiss Federal Elections. In: Tambouris, E., Macintosh, A., Glassey, O. (eds.) ePart 2010. LNCS, vol. 6229, pp. 211–224. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Mendez, F.: Matching voters with political parties and candidates: An empirical test of four algorithms. International Journal of ElectronicGovernance (2012)Google Scholar
  8. 8.
    Pianzola, J., Trechsel, A.H., Schwerdt, G., Vassil, K., Alvarez, R.M.: The Effect of Voting Advice Applications (VAAs) on Political Preferences. Evidence from a Randomized Field Experiment. In: Annual Meeting of the American Political Science Association (2012), Available at SSRN: http://ssrn.com/abstract=2108095
  9. 9.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook, pp. 39–71. Springer, Heidelberg (2011)CrossRefMATHGoogle Scholar
  10. 10.
    Tsapatsoulis, N., Georgiou, O.: Investigating the Scalability of Algorithms, the Role of Similarity Metric and the List of Suggested Items Construction Scheme in Recommender Systems. International Journal on Artificial Intelligence Tools 21(4), 12–40 (2012)CrossRefGoogle Scholar
  11. 11.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn., pp. 3–9. Morgan Kaufmann (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marilena Agathokleous
    • 1
  • Nicolas Tsapatsoulis
    • 1
  • Ioannis Katakis
    • 1
  1. 1.LimassolCyprus

Personalised recommendations