Modeling the Dynamics of Online News Reading Interests

Abstract

Online news readers exhibit a very dynamic behavior. News publishers have been investigating ways to predict such changes in order to adjust their recommendation strategies and better engage the readers. Existing research focuses on analyzing the evolution of reading interests associated with news categories. Compared to these, we study also how relations among news interests change in time. Observations over a 10-month period on a German news publisher indicate that overall, the relations amid news categories change, but stable periods spanning months are also found. The reasons of these changes and how news publishers could integrate this knowledge in their solutions are subject to further investigation.