Abstract
In the recent times Voting Advice Applications (VAAs) have become a widespread online tool for electoral campaigns in Europe. These online tools are designed to suggest voters the best suitable political party that matches their policies. In typical VAAs answers are collected for policy based questions from the candidate/party and also the voter, then these answers are compared and the user is suggested with the party/candidate whose answers matches the most. But there are chances of over promising answers form the party. This paves the way for a collaborative recommendation in VAAs, called Social VAAs (SVAA). In SVAA users are suggested with parties that other similar users prefer. Motivated with the goodness of fuzzy soft sets and various classifiers in the previous work, an approach combining both fuzzy soft set and machine learning is proposed so as to bring together the goodness of both. This paper explains and evaluates the combined approach and is found to perform the preceding methods.
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Nagarjan, S., Mohamed, A. (2020). A Combined Fuzzy Soft Set—Machine Learning Approach for Effective Party Recommendation. In: Kumar, A., Paprzycki, M., Gunjan, V. (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer, Singapore. https://doi.org/10.1007/978-981-15-1420-3_44
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DOI: https://doi.org/10.1007/978-981-15-1420-3_44
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