FindMoviez: A Movie Recommendation System

Movie recommendation has become one of the most efficient ways of making the user experience more personalized and connecting the user with the movies that the user might like. In this paper, FindMoviez, a movie recommendation engine has been introduced, which is based on a combination of two recommendation algorithms, implemented in a web application. The three sub-data sets (i.e.m ratings, users, and metadata) of the famous Movielens data set have been used. A combination of item–item collaborative filtering and genre based using the average weighted rating method has been used. These algorithms have been modified in a way where the user is always recommended movies, and even if one of the above algorithms fails, the other comes into play making this product more reliable for the user. Thus, the user can completely relies on this product for genuine movie recommendations.