Movie Recommendation System Using Machine Learning

Movie Recommendation System Using Machine Learning

(Content-based Approach)

Badal Chauhan & Shubham Sharma

Student1, and Student,2 Master of Computer Application, Graphic Era Hill University (UGC Affiliated) Dehradun, Uttarakhand, India

ABSTRACT

Movie recommendation systems are essential in today's world to provide personalized movie suggestions to users, reducing the time they spend searching for content. While Content-based Filtering is a popular technique for making recommendations, existing research uses a single text to vector conversion technique and a single method to find the similarity between vectors. In this research paper, we propose a novel approach that employs multiple text to vector conversion techniques and combines the results of several algorithms to generate the final movie recommendation list. This hybrid approach using Content-based Filtering technique enhances the accuracy of the recommendation system, providing users with more relevant movie suggestions. Through experiments on a real-world dataset, we demonstrate that our proposed approach outperforms the traditional Content-based Filtering technique and other state-of-the-art recommendation methods. Our research contributes to the field of machine learning-based recommendation systems and provides insights for future research in this domain.

Keyword: - Movie Recommendations, Content-based Filtering, Text to vector, Vector similarity.