In a news recommender system, a reader's preferences change over time . Some preferences drift quite abruptly (short-term preferences), while others change over a longer period of time (long-term preferences). Although the existing news recommender systems consider the reader's full history, they often ignore the dynamics in the reader's behavior . Thus, they cannot meet the demand of the news readers for their time-varying preferences . In addition, the state-of-the-art news recommendation models are often focused on providing accurate predictions, which can work well in traditional recommendation scenarios . However, in a news recommender system, diversity is essential, not only to keep news readers engaged, but also to play a key role in a democratic society . In this PhD dissertation, our goal is to build a news recommender system to address these two challenges . Our system should be able to: (i) accommodate the dynamics in reader behavior; and (ii) consider both accuracy and diversity in the design of the recommendation model . Our news recommender system can also work for unprofiled, anonymous and short-term readers, by leveraging the rich side information of the news items and by including the implicit feedback in our model . We evaluate our model with multiple evaluation measures (both accuracy and diversity-oriented metrics) to demonstrate the effectiveness of our methods.