U-Net-based Recommender Systems for Political Election System using Collaborative Filtering Algorithms

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency.Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics.This study focuses on holding elections with vague voter and candidate preferences.Collaborative user ratings are used by filtering algorithms to provide suggestions.To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems.Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services.To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies.This study provides a framework for an electronic voting advisory system that uses machine learning.