Abstract:

The popularity of streaming platforms has increased significantly in recent years, leading to a vast amount of content being available for users to choose from. With this influx of content, users often struggle to find movies that are tailored to their preferences. In response to this problem, this study aims to develop a recommendation system for personalized movie suggestions on streaming platforms.

The study begins with an exploration of the importance of personalized movie recommendations and the challenges faced by users in navigating through the vast library of content available on streaming platforms. The objectives of the study are then outlined, which include designing and implementing a recommendation system that leverages user preferences and viewing history to provide personalized movie suggestions.

The study hypothesis posits that a personalized recommendation system will lead to increased user engagement and satisfaction with the platform. However, the study also acknowledges limitations, such as data privacy concerns and the potential for algorithm bias.

In the literature review, past research on recommendation systems and personalized movie suggestions is examined, providing a theoretical framework for the study. The research methodology section details the process of collecting and analyzing data to train and evaluate the recommendation system.

The discussion of findings chapter presents the results of the study, including the effectiveness of the recommendation system in providing personalized movie suggestions. Finally, the summary, recommendation, and conclusion chapter concludes the study by highlighting the implications of the research and providing recommendations for future work in this area.

Table of Contents:

Chapter 1: Introduction
1.1 Significance of study
1.2 Objectives of study
1.3 Study hypothesis
1.4 Limitation of study

Chapter 2: Literature Review
2.1 Recommendation systems
2.2 Personalized movie suggestions
2.3 Challenges in movie recommendations

Chapter 3: Research Methodology
3.1 Data collection
3.2 Data analysis
3.3 System design

Chapter 4: Discussion of Findings
4.1 Evaluation of recommendation system
4.2 User feedback
4.3 Algorithm performance

Chapter 5: Summary, Recommendation, and Conclusion
5.1 Summary of findings
5.2 Recommendations for future research
5.3 Conclusion

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