  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.
