In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that the user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen based on the short-term user profile. Extensive empirical experiments on a collection of news articles obtained from various popular news websites demonstrate the efficacy of our method.