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Introduction

In recent years, the job market has become increasingly competitive, with job seekers facing challenges in finding the right job opportunities that match their skills, experience, and preferences. Job portals have emerged as a popular platform for job seekers to search for and apply to job openings. However, the abundance of job listings on these portals can be overwhelming for users, making it difficult for them to find relevant job opportunities efficiently. Recommender systems have been proposed as a potential solution to help job seekers discover job opportunities that align with their profile and preferences.

This thesis aims to explore the use of recommender systems in job portals to enhance the job search experience for users. The goal is to develop a recommendation system that can effectively match job seekers with job openings based on their skills, experiences, and preferences. By doing so, this research seeks to improve the efficiency and effectiveness of the job search process for users, ultimately leading to better job matching outcomes.

This introduction chapter provides an overview of the research topic, discussing the background of the study, the problem statement, the objectives of the study, the limitations and scope of the study, the significance of the study, the structure of the thesis, and the definition of key terms.

Chapter 1: 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 2: Literature Review
2.1 Introduction to Recommender Systems
2.2 Recommender Systems in Job Portals
2.3 Types of Recommender Systems
2.4 Challenges in Job Recommendation
2.5 User Preference Modeling
2.6 Collaborative Filtering Techniques
2.7 Content-Based Filtering Techniques
2.8 Hybrid Recommender Systems
2.9 Evaluation Metrics for Recommender Systems
2.10 Comparative Analysis of Recommender Systems

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Engineering
3.5 Algorithm Selection
3.6 Model Training and Evaluation
3.7 Performance Metrics
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Model Performance
4.2 User Satisfaction
4.3 Impact on Job Matching
4.4 Comparison with Existing Systems
4.5 Scalability and Performance
4.6 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Practice
5.4 Limitations and Future Research Directions
5.5 Conclusion

Thesis Overview: Recommender Systems for Job Portals

Recommender systems have gained prominence in various domains as a means to personalize recommendations and enhance user experience. In the context of job portals, recommender systems can play a crucial role in improving the job search process for users by providing personalized job recommendations based on their preferences and profile. This thesis aims to investigate the effectiveness of recommender systems in job portals and develop a recommendation system that can match job seekers with relevant job openings.

The thesis begins with an introduction chapter that provides an overview of the research topic, discussing the background of the study, the problem statement, the objectives of the study, the limitations and scope of the study, the significance of the study, the structure of the thesis, and the definition of key terms. The literature review chapter explores the existing literature on recommender systems, focusing on the types of recommender systems, challenges in job recommendation, user preference modeling, collaborative filtering techniques, content-based filtering techniques, hybrid recommender systems, and evaluation metrics for recommender systems.

The research methodology chapter details the research design, data collection, data preprocessing, feature engineering, algorithm selection, model training and evaluation, performance metrics, and ethical considerations. The discussion of findings chapter presents the results of the study, including model performance, user satisfaction, impact on job matching, comparison with existing systems, scalability and performance, and future research directions. The conclusion and summary chapter summarizes the findings, discusses the contributions of the study, implications for practice, limitations, and future research directions.

Overall, this thesis aims to contribute to the growing body of research on recommender systems for job portals and provide insights into how these systems can enhance the job search experience for users. By developing an effective recommendation system, this research seeks to improve the job matching outcomes for job seekers and help them find relevant job opportunities more efficiently.

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