Recommender System with User-Based and Item-Based Collaborative Filtering on Twitter using K-Nearest Neighbors Classification

Abstract

Netflix is one of the most widely used applications for watching movies online. There are various movie titles that can be watched by users, so a recommendation system is needed to help users who feel confused in choosing movie titles. Twitter is a social media used to express ideas, thoughts, and feelings. Not a few Twitter users who conduct movie discussions, with the movie discussion can be converted into a rating that can be used in the recommendation system. Collaborative Filtering is one of the methods of the recommendation system, by recommending based on the similarity between users (user-based) and based on items that have similarities with user-selected items (item-based). In this research, the Collaborative Filtering method is combined with K-Nearest Neighbors classification which obtains an RMSE value for user-based 1.8244 and item-based 0.5449. K-Nearest Neighbors gets 91.22% precision and 91.07% recall for user-based, while item-based gets 89.44% precision and 91.22% recall with the optimal K as a parameter is 3.