Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/3667
Title: Clustering online poll data: towards a voting assistance system
Authors: Mendez, Fernando 
Tsapatsoulis, Nicolas 
Katakis, Ioannis 
Triga, Vasiliki 
Djouvas, Constantinos 
Keywords: Assistance system
Weighted mean
Semantics
Online poll
Voting advice applications
Issue Date: 2012
Publisher: IEEE Xplore
Source: 7th International Workshop on Semantic and Social Media Adaptation and Personalization, Luxembourg, 3-4 December, 2012
Abstract: Voting advice applications (VAA) are very recently developed in order to aid users in deciding what to vote in elections. Every user is presented with a set of important issues and she is asked to submit her opinion by selecting one of a predefined set of answers (e.g. agree/disagree). The VAA gathers the same information for all candidates that are about to compete in the elections. Hence, it can provide recommendation to users: the candidates that agree with the user on these selected issues. In this paper, we propose a collaborating filtering approach for providing such suggestions. Like-minded users are clustered together based on their profiles (views on the selected issues) and voting recommendation is provided to a user by the members of the nearest (to her profile) cluster. We observe that this method produces more effective recommendations by utilizing two different measures: accuracy and weighted mean rank. Furthermore, the proposed method provides with important insight and summarization information about the electorate's opinion. This research is based on new data gathered by the voting advice application Choose4Greece which was widely used for the most recent elections in Greece.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/3667
DOI: 10.1109/SMAP.2012.19
Rights: © IEEE
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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