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.
Inhaltsverzeichnis (Table of Contents)
- Chapter 1: Introduction
- Context
- Motivation
- Structure
- Chapter 2: State of the Art
- Methodologies
- Methodology 1 – Reinforcement Learning
- Methodology 2 - Q-Learning
- Methodology 3 - Collaborative User Feedback
- Methodology 4 - User Profile Construction
- Related Work
- News Recommendation System for Social Networks (Agarwal et al, 2009)
- Personalized Online News Recommendation System (Saranya, 2012)
- SCENE News Recommendation System (Li et al 2011)
- Chapter 3: Work Plan
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This research study aims to develop an online news recommendation system (ONRS) that provides a highly personalized news reading experience. The study explores various methodologies and perspectives, including reinforcement learning, Q-Learning, collaborative filtering, and user profiling, to implement the ONRS system.
- Personalized news recommendations
- Content filtering and provisioning
- User behavior and preferences
- Reinforcement learning and Q-Learning
- User profiling and collaborative filtering
Zusammenfassung der Kapitel (Chapter Summaries)
- Chapter 1: Introduction
- Chapter 2: State of the Art
- Chapter 3: Work Plan
This chapter introduces the research context and motivation behind the development of an online news recommendation system. It highlights the challenges associated with providing personalized news in the rapidly evolving online landscape. The chapter also provides a brief overview of the structure of the dissertation.
This chapter delves into existing methodologies and related research in the domain of online news recommendation systems. It explores various approaches such as reinforcement learning, Q-Learning, collaborative user feedback, and user profile construction. The chapter also provides a critical analysis of existing news recommendation systems, including those developed for social networks and personalized news platforms.
This chapter outlines the proposed work plan for the research study, detailing the specific steps and methodologies involved in developing and implementing the ONRS system. It provides a roadmap for the research process, including data collection, system design, and evaluation methods.
Schlüsselwörter (Keywords)
This research focuses on the development of an online news recommendation system. Key topics include content filtering, reinforcement learning, and user profiling. The study aims to address the challenge of providing highly personalized news recommendations by utilizing user-specific information and various machine learning techniques.
- Citar trabajo
- Anonym (Autor), 2018, Online News Recommendation Systems in Machine Learning, Múnich, GRIN Verlag, https://www.grin.com/document/1325265
