A Hybrid Approach Towards a Movie Recommendation System

Online streaming media platforms are booming these days. They provide online services that cater specifically to their users and provide them with some much-needed entertainment. But when it’s time for a short break just to freshen up the mind, everyone prefers choosing from the options that are just a click away, what could be better than a user being recommended their favorite songs to play, movies to watch during a break. This is where highly advanced and convenient recommendation systems come into play. Recommendation systems solve this problem by analyzing the bulk of dynamically generated data to provide customized suggestions to the users. With the day-to-day increasing popularity and demand of such services, and with increasing competition in this massive service-oriented sector, a highly functional and effective recommendation system could provide a significant marketing edge to a company over its competitors, as a wide range of products, services and their substantial amount of information are available on the service provider company’s website and as a result, the users struggle to find relevant information matching their preferences. Thus, we have tried to design a framework for a movie recommendation system by using and testing various classical machine learning models like Content-Based Filtering and Collaborative Filtering to recommend the best movies based on user ratings, user watch history, movie data, etc., that are more likely to be watched by the customer. We have also combined these models to create a Hybrid model which provides better recommendations. This could help in improving the revenue earned by the media platforms, which in turn is the main purpose of any recommendation system.