Preference Based Personalized News Recommender System

News reading has changed from the traditional model of hardcopy newspapers to online news access. Thousands of news sources are available on internet each having millions of articles to choose from, leaving users tangled to find out a relevant article that matches their interests and liking. Recommender Systems can be used as a solution to this information overload problem by identifying the interest areas of a user by creating user profiles, maintaining those profiles to keep accommodating changing user interests and presenting a set of recent news articles formed as recommendations based on those user profiles. This paper presents an algorithm, which requests one time input from users (during the signup) about their preference of news categories (like Sports, Entertainment etc.), which they would like to subscribe and creates a personalized profile for each user. Subsequently, it requests an optional feedback on the recommended articles, to intelligently update user profiles, and recommend relevant articles to them, based on their changing interests. The paper also presents a simulation of the proposed algorithm on various use cases to depict the correctness and robustness of the algorithm. Also, it gives a brief idea about implementation details and challenges associated with the algorithm.