Movie Recommendation System Integrated With YouTube API
Year: 2025
Type: article
Abstract: This project focuses on building a movie recommendation system that helps users discover films similar to the ones they already enjoy by using a content-based filtering approach. The system relies on the TMDB dataset, which provides detailed information such as genres, overviews, cast details, and keywords. These features are compared to identify how closely one movie matches another, allowing the system to generate personalized and accurate recommendations. The system is integrated with the YouTube API, enabling users to watch trailers of the recommended movies directly within the platform. This creates a smoother and more interactive movie-exploration experience by combining similarity-based suggestions with instant access to official trailers. Keywords: Content-Based Filtering, TMDB dataset, Cosine Similarity, Movie Recommendation System, YouTube API, AI
Authors: Rohan, Rajendra Patil, Amit Shivaji Powar, Mrs. V. S. Mali
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7106657232
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