• Manju Arora
• Shivam Dimri
• Harsh Chauhan

This research investigates the enhancement of movie recommendations using collaborative filtering, emphasizing the application of user preferences for improved accuracy. "Cinematic Synergy" analyzes existing recommendation systems, critiques their methodologies, and thoroughly examines the functionalities of collaborative filtering algorithms. By assessing its performance in the context of movie recommendations, this study identifies challenges and proposes innovative solutions to refine the user experience. The findings of this research offer valuable insights for developers and industry professionals, aiming to elevate collaborative filtering for more effective and user-centric movie recommendations. This project introduces a novel feature of suggesting movies to users based on their selected genres. Additionally, the project displays trending popular movies below the recommendations provided, facilitating easy access for users to explore the world of movies.

Clustering, collaborative filtering, Vectorization, Cosine similarity recommendation

"Cinematic Synergy: Optimizing Movie Recommendations through Collaborative Filtering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.b656-b665, February-2024, Available :http://www.jetir.org/papers/JETIR2402173.pdf

"Cinematic Synergy: Optimizing Movie Recommendations through Collaborative Filtering", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppb656-b665, February-2024, Available at : http://www.jetir.org/papers/JETIR2402173.pdf