A News Recommender System Considering Temporal Dynamics and Diversity
This work addresses the challenge of improving news recommendation for readers by handling time-varying preferences and diversity, though it appears incremental as it builds on existing models.
The paper tackled the problem of news recommendation by addressing temporal dynamics in reader preferences and the need for diversity, resulting in a system that accommodates behavior changes and balances accuracy with diversity metrics.
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.