MOVIE RECOMMENDATION SYSTEM USING MACHINE LEARNING

MOVIE RECOMMENDATION SYSTEM USING MACHINE LEARNING

Jitendra Kumar*1, Er. Raghvendra Singh*2

*1 Undergraduate Student, Department of Bachelor of Computer Application, Shri Ramswaroop Memorial College of Management, Lucknow, Uttar Pradesh, India.

*2 Assistant Professor, Head of Department of Bachelor of Computer Application, Shri Ramswaroop Memorial College of Management Lucknow, Uttar Pradesh, India.

ABSTRACT

Movie recommendation systems powered by Machine Learning have revolutionized the entertainment industry by providing personalized movie suggestions to users. When a new user visits any website without any past history, the site can approach recommending products through various strategies. One approach is to suggest popular products that are in high demand, leveraging the wisdom of the crowd to guide new users. Another strategy could involve recommending products that maximize profit for the business, focusing on items with high revenue potential.

In the realm of recommender systems, the main approaches are commonly used in movie recommendation system are Content-Based Filtering is a distinct approach in recommendation systems that leverages user data and preferences to provide personalized suggestions. Unlike demographic-based filtering, which makes generalized recommendations based on broad user segments.

Content-Based Filtering delves deeper into an individual's interests and behaviors, this method tailors suggestions to individual tastes, focusing on personalized recommendations.

Collaborative Filtering, the third approach, groups similar users together based on their behavior and preferences, using this collective data to make recommendations to individual users.

Keywords - Movie recommendation systems, Machine Learning, Personalized suggestions, content-based filtering, demographic-based filtering, Behaviour and preferences.