MOVIE RECOMMENDER SYSTEM USING COLLABORATIVE FILTERING

Abstract

Movies are one of the sources of entertainment, but the problem is how to find what you need from the millions of contents that is increasing every year. However, in these cases, the recommendation system is much more convenient. The purpose of this article is to improve the accuracy and performance of conventional filtering techniques. Although multiple methods are used to implement a recommendation system, content-based filtering is the simplest method. It accepts the user's input, rechecks his/her history/past behaviour, and recommends a list of similar movies. In this article, in order to prove the effectiveness, compared with content-based filtering, K-NN algorithm and collaborative filtering mainly focus on improving the accuracy of the results. Cosine similarity is used as the accuracy of cosine angle and the equidistance of movies remain almost the same.