This research was responsible for the development of a method for recommending news in online newspapers. This study takes into consideration that each reader has specific needs and interests when reading online newspapers, and it is a challenge to bring personalized and individualized information, in order to meet each reader's needs. The main goal here was solving or minimizing this problem when there is a new reader, because the system has little or no information over the reader’s preferences. This descriptive research used as a subject a new reader from a news portal and all data collected from the web browsing experience was performed without that user’s knowledge. The research may be characterized as applied, since it generated knowledge enough for solving the problem of online newspaper readers. A quantitative approach was adopted, because the news recommended by the system were classified and the system’s accuracy was quantified comparing the system`s suggestions and the decisions made by the readers. The solution adopted involved accessing three different methods. The Bayesian network was adopted as the main method when generating news suggestions to the new reader and the excess of variables was clustered using the K-means algorithm. The probabilities missing on this network were captured through the EM algorithm (Expectation Maximization). This algorithm uses cases in which variables were used to learn how to predict their values when they are not being observed.