Collaborative Filtering for Personalized Movie Recommendations on Streaming Platforms

Recommendation engines have become an integral component of modern streaming platforms, enhancing user experience by delivering personalized suggestions. This study proposes a collaborative filtering-based recommendation system to predict and suggest highly-rated movies and TV shows tailored to individual user preferences. Utilizing Python and an integrated suite of statistical, data analytics, and machine learning libraries, the system analyzes users’ historical ratings and recent category search histories to generate recommendations. The dataset encompasses movie identifiers, genres, user ratings, and titles, forming a comprehensive foundation for accurate filtering. The collaborative filtering approach leverages user-item interactions to identify patterns, ensuring recommendations are both relevant and personalized. Key innovations of this system include the incorporation of genre-based search histories and a dynamic update mechanism to reflect evolving user preferences. Experimental results demonstrate the system’s effectiveness in predicting user satisfaction, with improved accuracy and relevance compared to baseline methods. This study highlights the potential of collaborative filtering in the domain of personalized entertainment while addressing challenges such as scalability and cold-start problems. The proposed system offers a robust and scalable framework that can be adapted to various streaming platforms, significantly enhancing user engagement and satisfaction. Future work will focus on integrating hybrid recommendation techniques to further improve prediction accuracy and user experience.