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Thesis Overview

Title: Recommender Systems for News Articles using Content-Based Filtering

Introduction

In today’s digital age, the vast amount of information available online can be overwhelming for users to navigate. Recommender systems offer a solution to this problem by providing personalized recommendations based on user preferences and behavior. This thesis focuses on the use of content-based filtering in recommender systems for news articles. Content-based filtering recommends items to users based on the similarity of the content of the items and a user’s preferences.

Chapter One: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter Two: Literature Review
2.1 Introduction to Recommender Systems
2.2 Types of Recommender Systems
2.3 Content-Based Filtering
2.4 News Article Recommender Systems
2.5 Evaluation Metrics for Recommender Systems
2.6 Challenges in News Article Recommendation
2.7 User Modeling in Recommender Systems
2.8 Personalization in News Article Recommendation
2.9 Ethical Considerations in Recommender Systems
2.10 Current Trends in Recommender Systems

Chapter Three: Research Methodology
3.1 Introduction to Research Methodology
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Extraction
3.5 Algorithm Selection
3.6 Model Training
3.7 Evaluation Method
3.8 Performance Metrics
3.9 Validation Techniques

Chapter Four: Discussion of Findings
4.1 Introduction to Discussion
4.2 Analysis of Experimental Results
4.3 Comparison of Algorithms
4.4 Implications of Findings
4.5 Recommendations for Future Research
4.6 Limitations of the Study

Chapter Five: Conclusion and Summary
5.1 Conclusion
5.2 Summary of Findings
5.3 Contributions of the Study
5.4 Practical Implications
5.5 Future Research Directions

This thesis aims to contribute to the existing body of knowledge on recommender systems for news articles using content-based filtering. By understanding the effectiveness of this approach and addressing the challenges in news article recommendation, this research can help improve user experience and engagement with online news content.

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