Movie Recommendation System by Machine Learning Model

Movie Recommendation System by Machine Learning Model

Anmol Rana, Shruti Puri, Nishant, Anush Sharma

HIET, Kangra, HP, India

Abstract: In today's modern age, recommendation systems have completely transformed how we find the content we love. Particularly for movie fanatic, who are often submerge by the immense range of choices available, these systems have become invaluable tools in simplifying decision-making. This paper aims to make it easier for users by introducing a new model that merges elements from both content-based and collaborative approaches, aiming to provide more precise and personalized movie recommendations compared to the traditional content-based methods. While content-based recommendation systems tend to keep users within their comfort zones and limit exploration of new options, our hybrid system aims to overcome these limitations. In this review, we delve into a thorough analysis of this innovative approach and explore its potential to transform the movie-watching experience.

Keywords: Cosine Similarity, Count Vectorization, Machine Learning, Deep Learning, Movie, Movie tags, Stemming