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Introduction

With the rise of online dating platforms, the need for effective recommender systems has become increasingly important. Recommender systems utilize user profiles and interaction data to suggest potential matches to users, ultimately improving the overall user experience and increasing the likelihood of successful matches. This thesis explores the use of recommender systems for online dating, focusing on the utilization of user profiles and interaction data to enhance the matchmaking process.

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 Online Dating Platforms
2.3 User Profiles in Online Dating
2.4 Interaction Data in Online Dating
2.5 Collaborative Filtering in Recommender Systems
2.6 Content-Based Filtering in Recommender Systems
2.7 Hybrid Recommender Systems
2.8 Evaluation Metrics for Recommender Systems
2.9 Challenges in Recommender Systems for Online Dating
2.10 Current Research Trends in Recommender Systems for Online Dating

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Algorithm Selection
3.5 Model Development
3.6 Model Evaluation
3.7 Ethical Considerations
3.8 Limitations of Research Methodology

Chapter 4: Discussion of Findings
4.1 Analysis of User Profiles
4.2 Analysis of Interaction Data
4.3 Comparison of Recommender System Algorithms
4.4 Impact of User Feedback on Recommendations
4.5 User Satisfaction and Success Rates
4.6 Personalization and Diversity in Recommendations
4.7 Practical Implications for Online Dating Platforms
4.8 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Recap of Research Objectives
5.2 Key Findings
5.3 Contributions to Literature
5.4 Practical Implications
5.5 Limitations and Future Research Directions
5.6 Conclusion

Thesis Overview

Recommender systems play a crucial role in enhancing the user experience on online dating platforms. By utilizing user profiles and interaction data, these systems can provide personalized recommendations that improve the matchmaking process. This thesis explores the use of recommender systems for online dating, focusing on the integration of user profiles and interaction data to enhance the effectiveness of the matchmaking process.

In chapter 2, the literature review provides an overview of recommender systems, online dating platforms, user profiles, interaction data, and various recommender system algorithms. Current research trends and challenges in recommender systems for online dating are also discussed.

Chapter 3 details the research methodology, including research design, data collection, data preprocessing, algorithm selection, model development, and model evaluation. Ethical considerations and limitations of the research methodology are also addressed.

In chapter 4, the discussion of findings includes an analysis of user profiles, interaction data, and recommendation algorithms. The impact of user feedback on recommendations, user satisfaction rates, and the practical implications for online dating platforms are also examined.

Chapter 5 concludes the thesis by summarizing the research objectives, key findings, contributions to literature, practical implications, limitations, and future research directions. Overall, this thesis provides valuable insights into the use of recommender systems for online dating, highlighting the importance of user profiles and interaction data in enhancing the matchmaking process.

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