"Movie Recommendation System" by Vinay Karre
 

Level of Education of Students Involved

Graduate

Faculty Sponsor

Jon Beagley

College

College of Arts & Sciences (CAS)

Discipline(s)

Data Science, Machine Learning

ORCID Identifier(s)

https://orcid.org/0009-0007-2395-8894

Presentation Type

Poster Presentation

Symposium Date

Spring 4-24-2025

Abstract

This project presents a movie recommendation system that leverages machine learning algorithms to suggest movies based on user input. By combining features such as lead actors, genres, and directors, the system creates a comprehensive profile for each movie. Using Count Vectorization and cosine similarity, the system calculates the similarity between movies and provides recommendations. The system normalizes movie titles to ensure robust search functionality and retrieves the most similar movies based on user searches. Implemented with Flask for the web interface and FlaskUI for desktop deployment, this system demonstrates the potential of machine learning in enhancing user experiences through personalized recommendations in the entertainment industry. The project showcases how integrating multiple features and advanced algorithms can significantly improve the accuracy and relevance of movie recommendations, making it easier for users to discover new movies that match their preferences.

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