Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily overwhelmed by information of little interest to them. News recommender systems help users manage this flood by recommending articles based on user interests rather than presenting articles in order of their occurrence. We present our research on developing personalized news recommendation system with the help of a popular micro-blogging service, "Twitter." News articles are ranked based on the popularity of the article identified from Twitter's public timeline. In addition, users construct profiles based on their interests and news articles are also ranked based on their match to the user profile. By integrating these two approaches, we present a hybrid news recommendation model that recommends interesting news articles to the user based on their popularity as well as their relevance to the user profile.Subjects Agents and Multi-Agent Systems, World Wide Web and Web Science Keywords Twitter, Personalized news recommendation, News recommender systems, User profile INTRODUCTIONOwing largely to the ever-increasing volume and sophistication of information on the web, we are able to access an enormous amount of data from around the globe. The downside of this information explosion is that users are often overwhelmed by information of little interest to them. The key challenge for the users is to find relevant information based on their interests. This problem has led to the evolution of recommender systems that help users find the information they need, based on their interests. Recommender systems proactively present users with information related to their interests rather than requiring the user to search for, and then filter through, information based on explicit queries.Many organizations use recommender systems to recommend various types of products to the user. For example, Netflix recommends movies to its users based on the user's movie ratings compared to other similar users' ratings. Amazon recommends various types of products such as gadgets, books, or movies and Pandora Radio recommends music based on a user's past history and preferences. In addition, news recommender systems that recommend news articles from around the globe have become popular. There are many online news services such as Google News and Yahoo News. However, with plenty of news available, the driving problem is to identify and recommend the most interesting articles to each user so that they are not swamped by irrelevant Distributed underCreative Commons CC-BY 4.0 information. These articles should be related to each user interests but also include those news stories that are generating a lot of interest around the globe.News recommender systems are broadly classified into two types, content-based filtering and collaborative filtering. Content-based filtering methods are based on the information and attributes of the product that is being recommended....

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