Film Flare: Movie Recommendation System Using Machine Learning
Year: 2025
Type: article
Abstract: In today's digital entertainment world, the abundance of content on various streaming platforms often leads to user indecision and dissatisfaction. As a solution, recommendation systems play a vital role in filtering content to match user interests. This research presents a robust hybrid recommendation model that merges collaborative and content-based filtering to enhance recommendation quality. Drawing on a rich dataset of 5,000 movies from Kaggle, the model incorporates user ratings and comprehensive movie metadata, including genres, cast, and plot keywords. By integrating both historical user behavior and movie content features, this hybrid approach alleviates the limitations of standalone methods and delivers a personalized viewing experience. The implementation, carried out using Python in Google Colab, validates the model's practicality and scalability through detailed evaluation and real-time demonstrations.
Authors: Adeeba Nousheen, Faiza Naaz, Arshiya Sadaff, Syeda Hifsa Naaz
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949652158
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