In today’s digital world where there is an endless variety of content to be consumed like books, videos, articles, movies, etc., finding the content of one’s liking has become an irksome task. On the other hand, digital content providers want to engage as many users on their service as possible for the maximum time. This is where recommendation system comes into picture where the content providers recommend users the content according to the users’ liking. In this paper, I have proposed a movie recommendation system. The objective is to provide accurate movie recommendations to users. Usually, the basic recommendation systems consider one of the following factors for generating recommendations; the preference of user (i.e. content based filtering). To build a stable and accurate recommendation system we have used collaborative filtering, cosine similarity and basic IMDB user’s rating-based techniques.
brijesh25/Movie-Recommendation-System
In today’s digital world where there is an endless variety of content to be consumed like books, videos, articles, movies, etc., finding the content of one’s liking has become an irksome task. On the other hand, digital content providers want to engage as many users on their service as possible for the maximum time. This is where recommendation system comes into picture where the content providers recommend users the content according to the users’ liking. In this paper, I have proposed a movie recommendation system. The objective is to provide accurate movie recommendations to users. Usually, the basic recommendation systems consider one of the following factors for generating recommendations; the preference of user (i.e. content based filtering). To build a stable and accurate recommendation system we have used collaborative filtering, cosine similarity and basic IMDB user’s rating-based techniques.
PythonApache-2.0