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.
Recommended Citation
Karre, Vinay, "Movie Recommendation System" (2025). Symposium on Undergraduate Research and Creative Expression (SOURCE). 1486.
https://scholar.valpo.edu/cus/1486