On the design of social voting recommendation applications

International Journal on Artificial Intelligence Tools

Volumn 24, Issue 3, 2015, Pages

  Tsapatsoulis, Nicolas ^a   Agathokleous, Marilena ^a   Djouvas, Constantinos ^a   Mendez, Fernando ^b  

^a CYPRUS UNIVERSITY OF TECHNOLOGY   (Cyprus)

^b UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

design; machine learning; Mahalanobis classifier; Voting advice applications

Indexed keywords

COLLABORATIVE FILTERING; DESIGN; LEARNING SYSTEMS;

BASIC PRINCIPLES; EUROPEAN COUNTRIES; MAHALANOBIS CLASSIFIERS; NUMBER OF SAMPLES; ON-LINE TOOLS; POLITICAL PARTIES; REAL WORLD SETTING; WEB BASED;

ELECTRONIC VOTING;

EID: 84930795142     PISSN: 02182130     EISSN: 17936349     Source Type: Journal    
DOI: 10.1142/s0218213015500098     Document Type: Article

References (30)

1. S. Abe, Support vector machines for pattern classification (Springer, 2010).
2. G. Adomavicius and A. Tuzhilin, Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, 2005 IEEE Transactions on Knowledge and Data Engineering 17(6) (2005) 734-749.
3. M. Agathokleous, N. Tsapatsoulis and I. Katakis, On the quantification of missing value impact on voting advice applications, in Proc. of EANN 2013, Part I, CCIS 383, eds. L. Iliadis, H. Papadopoulos and C. Jayne (Springer Berlin Heidelberg, 2013), pp. 496-505.
4. M. Agathokleous and N. Tsapatsoulis, Voting Advice Applications: Missing value estimation using matrix factorization and collaborative filtering, IFIP Advances in Information and Communication Technology, Vol. 412/2013 (Springer Berlin Heidelberg, 2013), pp. 20-29.
5. A. Baka, L. Figgou and V. Triga, Neither agree, nor disagree: A critical analysis of the middle answer category in voting advice applications, International Journal of Electronic Governance 5(3/4) (2012) 244-263.
6. L. Cedroni and G. Diego, eds., Voting Advice Applications in Europe: The State of the Art (ScriptaWeb, Napoli, 2010).
7. M. L. Eaton, Multivariate Statistics: A Vector Space Approach (Wiley, 1983).
8. D. Garzia and S. Marshall (Eds.), Matching Voters with Parties and Candidates (ECPR Press, Essex, 2014).
9. K. Gemenis, Estimating parties' policy positions through voting advice applications: Some methodological considerations, Acta Politica 48 (2013) 268-295.
10. K. Gemenis and C. Van Ham, Comparing methods for estimating parties' positions in voting advice applications, in Matching Voters with Parties and Candidates, eds. D. Garzia and S. Marshall (ECPR Press, Essex, 2014).
11. M. Germann, F. Mendez, J. Wheatley and U. Serdült, Spatial maps in voting advice applications: The case for dynamic scale validation, Acta Politica (to appear).
12. J. Han, M. Kamber and J. Pei, Data Mining: Concepts and Techniques (Morgan Kaufmann, 2006).
13. S. O. Haykin, Neural Networks and Learning Machines, 3rd edn. (Prentice Hall, 2008).
14. I. Katakis, N. Tsapatsoulis, V. Triga, C. Tziouvas and F. Mendez, Clustering Online Poll Data: Towards a Voting Assistance System, in Proc. of the 7th Semantic and Social Media Adaptation and Personalization Workshop (SMAP'12) (IEEE Press, 2012), pp. 54-59.
15. I. Katakis, N. Tsapatsoulis, F. Mendez, V. Triga and C. Djouvas, Social voting advice applications-Definitions, challenges, datasets and evaluation, IEEE Transactions on Cybernetics (to appear).
16. T. Louwerse and M. Rosema, The design effects of voting advice applications: Comparing methods of calculating matches, Acta Politica (2013), doi: 10.1057/ap.2013.30.
17. C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval (Cambridge University Press, 2008).
18. A. McCallum, K. Nigam and L. H. Ungar, Efficient clustering of high-dimensional data sets with application to reference matching, in Proc. of 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (ACM, 2000), pp. 169-178.
19. G. McLachlan, Discriminant Analysis and Statistical Pattern Recognition (Wiley-Interscience, 2004).
20. F. Mendez, Matching voters with political parties and candidates: An empirical test of four algorithms, International Journal of Electronic Governance 5(3/4) (2012) 264-278.
21. F. Mendez, What's behind a matching algorithm: A critical assessment of how VAAs produce voting recommendations, in Matching Voters with Parties and Candidates, eds. D. Garzia and S. Marshall (ECPR Press, Essex, 2014).
22. L. Teran, A. Ladner, J. Fivaz and S. Gerber, Using a fuzzy-based cluster algorithm for recommending candidates in E-elections, in Fuzzy Methods for Customer Relationship Management and Marketing: Applications and Classifications, eds. A. Meier and L. Donze (IGI Global, 2012).
23. V. Triga, U. Serdült and T. Chadjipadelis, Special Issue on Voting Advice Applications and State of the Art, International Journal of Electronic Governance 5(3/4) (Interscience, 2012).
24. V. Triga, Social Representations of Voting Advice Applications: A Comparative Analysis, in Matching Voters with Parties and Candidates, eds. D. Garzia and S. Marshall (ECPR Press, Essex, 2014).
25. N. Tsapatsoulis and O. Georgiou, 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) (2012) 12-40.
26. K. Van Camp, J. Lefevere and S. Walgrave, The content and formulation of statements in voting advice applications: A comparative analysis of 26 VAAs, in Matching Voters with Parties and Candidates, eds. D. Garzia and S. Marshall (ECPR Press, Essex, 2014).
27. K. Vassil, Voting Smarter? The Impact of Voting Advice Applications on Political Behavior, PhD Thesis, Department of Political and Social Sciences, European University Institute (2012).
28. S. Walgrave, M. Nuytemans and K. Pepermans, Voting aid applications and the effect of statement selection, West European Politics 32(6) (2009) 1161-1180.
29. J. Wheatley, C. Carman, F. Mendez and J. Mitchell, The dimensionality of the Scottish political space: Results from an experiment on the 2011 Holyrood elections, Party Politics (2012), doi: 10.1177/1354068812458614.
30. Y. Yue, T. Finley, F. Radlinski and T. Joachims, A support vector method for optimizing average precision, in Proc. of the 30th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (ACM, 2007), pp. 271-278.