Paper Contents
Abstract
This research presents the development of a content-based movie recommendation system that provides users with five relevant movie suggestions based on a single input movie title. Leveraging metadata such as genres, the system uses Term Frequency-Inverse Document Frequency (TF-IDF) vectorization to convert textual data into numerical representations. It then applies cosine similarity to identify relationships between movies, enabling efficient recommendation generation. Implemented in Python with libraries like Pandas and scikit-learn, the system is lightweight, scalable, and effective. Although limited to genre-based data, the project demonstrates the potential of content-based filtering in aiding users to navigate vast movie catalogs. Future improvements include integrating additional metadata, such as plots and cast, or incorporating collaborative filtering for more personalized recommendations..
Copyright
Copyright © 2024 Ms.Meenu. This is an open access article distributed under the Creative Commons Attribution License.
