Development of Online Movie Recommendation System based on Neighborhood-based Collaborative Filtering

Abstract

The recommendation system integrated in movie streaming provides relevant information to viewers predicted by viewers’ past behaviors. There are basically two methods, Content-Based Filtering and Collaborative Filtering. In this article, our focus is on the second method which is based on memory, namely Neighborhood-based Collaborative Filtering (NBCF), to make movie recommendations to users given users’ information. Simultaneously, we have built an online movie website and integrated the movie recommendation system based on NBCF to assist users in movie selection. In the process of building the website, apart from building diagram of movie recommendation system’s functions, class diagram of movie recommendation function, sequence diagram of movie recommendation function, we also build a user-recommended movie model based on the Movies Lens[9] dataset for a fairly high accuracy, which is 99%.