Movie recommendation system

Abstract

Recommendation systems are an integral part of information filtering system in data science, that are widely used in order to identify the pattern a user would likely choose on the basis of the previous choices of the user as well as from studying the pattern in which others have chosen. For a fact, the recommendation can never be a cent percent correct at providing recommendations to the user but can be close enough to please them to a certain extent. Thus, the same is widely used in the industries these days to get higher profit and have a good hold in the market. In the spread of information, how to quickly find one’s favorite movie in a large number of movies collection become a very important issue. A movie recommendation system can play an important role especially when the user has no clear target movie. This System would benefit those users who have to use search engines to locate relevant content. They have to scroll through pages of results to find relevant content. Rather than searching for a similar movie, the users of this system would be directly taken to relevant movies matching their interests and preferences. In this project we will work on Content-Based Movie Recommendation Systems It uses attributes such as genre, director, description, actors, etc. for movies, to make suggestions for the users. The intuition behind this sort of recommendation system is that if a user liked a particular movie or show, he/she might like a movie or a show similar to it.