Abstract or Introduction
Bearing in mind the increasing need for access to personalized news, the current research study aims at developing an online news recommendation system that could offer an optimum online news reading experience in a highly personalized fashion. The study considers major methodologies and perspectives, such as reinforced learning, Q-Learning, Collaborative Filtering and User Profiling, within this domain in order to implement the ONRS system.
Online news reading has gained more attention in recent years than ever, particularly based on the increasing dependence of users on smartphones and the internet. Leading a busy lifestyle, end-users find it hard to search for relevant news articles online, and require tools that could provide them with the most needed news feed on the go. Although legacy news recommendation systems do exist, yet they do not offer optimum efficiency and accuracy.
- Quote paper
- Anonymous, 2018, Online News Recommendation Systems in Machine Learning, Munich, GRIN Verlag, https://www.grin.com/document/1325265
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