Comparison News Recommendation Article using Content Based Filtering And Collaborative Filtering Based On News Portal Indonesia

Abstract

The goals of this thesis were to compare the content based filtering and collaborative filtering to get the best way to recommend the article from new portal in Indonesia. Data was collected from the activity of user who interacts with the article, after that the data is processing using content based filtering and collaborative filtering to get top 5 and top 10 of recommendation and calculate the recall of recommended the article. It concluded that the content based filtering is the best method to recommend the article on new portal in Indonesia

Keywords:Content based filtering, collaborative filtering, recommender system, data mining, text mining