Movie Recommendation System Using Artificial Intelligence and Machine Learning

 Vamsi Krishna¹, Voddi Jayanth²

¹ Assistant Professor

² MCA Final Semester, Master of Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam,

Andhra Pradesh, India

ABSTRACT

A Movie Recommendation System is a data-driven application that provides personalized movie suggestions based on user preferences and interests. With the rapid growth of streaming platforms, users often face challenges in selecting suitable content from a large collection of movies. This system addresses that problem by analyzing user behavior and movie information to recommend relevant movies efficiently. It uses machine learning techniques such as content-based filtering and collaborative filtering to identify patterns in user preferences and generate accurate recommendations.The system utilizes datasets like MovieLens, which contain movie details and user ratings. The data is preprocessed to improve quality and remove inconsistencies before model development. Features such as genre, cast, director, and ratings are analyzed, and techniques like Cosine Similarity are used to measure the similarity between movies. Based on user input and historical data, the system generates a ranked list of recommended movies. Developed using Python and libraries such as Pandas, NumPy, and Scikit-learn, the recommendation system demonstrates the practical application of machine learning in delivering personalized entertainment experiences.

Keywords: Movie Recommendation System, Machine Learning, Content-Based Filtering, Collaborative Filtering, Cosine Similarity, MovieLens Dataset, Personalized Recommendations, Python, User Ratings, Data Preprocessing.