Voting Advice Applications: Missing Value Estimation Using Matrix Factorization and Collaborative Filtering

  • Marilena Agathokleous
  • Nicolas Tsapatsoulis
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 412)


A Voting Advice Application (VAA) is a web application that recommends to a voter the party or the candidate, who replied like him/her in an online questionnaire. Every question is responding to the political positions of each party. If the voter fails to answer some questions, it is likely the VAA to offer him/her the wrong candidate. Therefore, it is necessary to inspect the missing data (not answered questions) and try to estimate them. In this paper we formulate the VAA missing value problem and investigate several different approaches of collaborative filtering to tackle it. The evaluation of the proposed approaches was done by using the data obtained from the Cypriot presidential elections of February 2013 and the parliamentary elections in Greece in May, 2012. The corresponding datasets are made freely available to other researchers working in the areas of VAA and recommender systems through the Web.


Missing values collaborative filtering recommender systems voting advice applications 


  1. 1.
    Andridge, R.R., Little, R.J.A.: A review of hot deck imputation for survey nonresponse. International Statistical Review 78(1), 40–64 (2010)CrossRefGoogle Scholar
  2. 2.
    Bilmes, J.: A gentle tutorial of the EM algorithm and its applications to parameter estimation for Gaussian mixture and hidden Markov models. Technical report, International Computer Science Institute (1998), (retrieved) (last access March 2013)
  3. 3.
    Bottou, L.: Large-Scale Machine Learning with Stochastic Gradient Descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010. Springer, Heidelberg (2010)Google Scholar
  4. 4.
    Bremner, D., Demaine, E., Erickson, J., Iacono, J., Langerman, S., Morin, P., Toussaint, G.: Output- sensitive algorithms for computing nearest-neighbor decision boundaries. Discrete and Computational Geometry 33(4), 593–604 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Cedroni, L., Diego, G. (eds.): Voting Advice Applications in Europe: The State of the Art. ScriptaWeb, Napoli (2010)Google Scholar
  6. 6.
    Enders, C.K.: A primer of maximum likelihood algorithms available for use with missing data. Structural Equation Modeling 8, 128–141 (2001)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Gamerman, D., Lopes, H.F.: Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, 2nd edn. Chapman and Hall/CRC (2006)Google Scholar
  8. 8.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann (2011)Google Scholar
  9. 9.
    Herlocker, J.L., Konstan, J.A., Riedl, J.T.: An empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Information Retrieval 5(4), 287–310 (2002)CrossRefGoogle Scholar
  10. 10.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press (2010)Google Scholar
  11. 11.
    Kim, K., Ahn, H.: A recommender system using GA K-means clustering in an online shopping market. Expert Systems with Applications: An International Journal 34(2), 1200–1209 (2008)CrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Salakhutdinov, R., Mnih, A.: Probabilistic Matrix Factorization. In: Advances in Neural Information Processing Systems (NIPS 2007), pp. 1257–1264. ACM Press (2008)Google Scholar
  14. 14.
    Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Application of Dimensionality Reduction in Recommender System - A Case Study. In: Workshop on Web Mining for e-Commerce: Challenges and Opportunities (WebKDD). ACM Press (2000)Google Scholar
  15. 15.
    Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating Word of mouth. In: ACM CHI 1995 Conference on Human Factors in Computing Systems, pp. 210–217. ACM Press (1995)Google Scholar
  16. 16.
    Tansey, S.D., Jackson, N.: Poltics: the basics, 4th edn. Routledge (2008)Google Scholar
  17. 17.
    Triga, V., Serdult, U., Chadjipadelis, T.: Voting Advice Applications and State of the Art: Theory, Practice, and Comparative Insights. International Journal of Electronic Governance 5(3/4) (2012)Google Scholar
  18. 18.
    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), 19–26 (2012)CrossRefGoogle Scholar
  19. 19.
    Ungar, L.H., Foster, D.P.: Clustering Methods for Collaborative Filtering. In: AAAI Workshop on Recommendation Systems, pp. 1–16. AAAI Press (1998)Google Scholar
  20. 20.
    Vogiatzis, D., Tsapatsoulis, N.: Missing Value Estimation for DNA Microarrays with Mutliresolution Schemes. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 141–150. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Walgrave, S., Van Aelst, P., Nuytemans, M.: Do the Vote Test: The Electoral Effects of a Popular Vote Advice Application at the 2004 Belgian Elections. Acta Politica 43(1), 50–70 (2008)CrossRefGoogle Scholar
  22. 22.
    Wall, M.E., Rechtsteiner, A., Rocha, L.M.: Singular value decomposition and principal component analysis. In: Berrar, D., Dubitzky, W., Granzow, M. (eds.) A Practical Approach to Microarray Data Analysis, pp. 91–109. Kluwer, MA (2003)CrossRefGoogle Scholar
  23. 23.
    Zhou, T., Shan, H., Banerjee, A., Sapiro, G.: Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information. In: SIAM International Conference on Data Mining, pp. 403–414. SIAM / Omnipress (2012)Google Scholar
  24. 24.
    Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R.: Large-Scale Parallel Collaborative Filtering for the Netflix Prize. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 337–348. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

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

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