A newspaper divided into various sections like a city, sports, editorial, international, national, entertainment etc. All the above sections have equal importance and different user followers for different sections. Sometimes there may be a possibility that they may consist of relevant information but in different sections and different newspapers. News Recommendation System can overcome this problem and suggest relevant news according to user preference and popularity factor. This research paper investigates the need for news recommendation using a machine learning approach to make it more efficient and better. Hybrid Approach can help to recommend news to users based on Supervised Machine Learning and Term Frequency-Inverse Document Frequency (TF-IDF)

Keywords : Machine Learning, Naïve Bayes, News Recommen- Dation, TF-IDF