Using a Fuzzy-Based Cluster Algorithm for Recommending Candidates in E-Elections

Abstract

The use of the Internet now has a specific purpose: to find information. Unfortunately, the amount of data available on the Internet is growing exponentially, creating what can be considered a nearly infinite and ever-evolving network with no discernable structure. This rapid growth has raised the question of how to find the most relevant information.Many different techniques have been introduced to address the information overload, including search engines, semantic web, and recommender systems, among others.Recommender systems are computer-based techniques that are used to reduce information overload and recommend products likely to interest a user when given some information about the user's profile. This technique is mainly used in eCommerce to suggest items that fit a customer's purchasing tendencies.The use of recommender systems for eGovernment is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on eGovernment services. More specifically, eDemocracy aims to increase citizens' participation in democratic processes through the use of information and communication technologies.In this chapter, an architecture of a recommender system that uses fuzzy clustering methods for eElections is introduced. In addition, a comparison with the smartvote system, a Web-based Voting Assistance Application (VAA) used to aid voters in finding the party or candidate that is most in line with their preferences, is presented.

Aligning Political Options and Aggregated Personal Opinions on the Issues

“…Everyone's preferences now factor in the decision process. And it turns out that there is already work in using AI to match voter preferences to platforms, as in [14].…”