  News recommendation is often modeled as a sequential recommendation task,
which assumes that there are rich short-term dependencies over historical
clicked news. However, in news recommendation scenarios users usually have
strong preferences on the temporal diversity of news information and may not
tend to click similar news successively, which is very different from many
sequential recommendation scenarios such as e-commerce recommendation. In this
paper, we study whether news recommendation can be regarded as a standard
sequential recommendation problem. Through extensive experiments on two
real-world datasets, we find that modeling news recommendation as a sequential
recommendation problem is suboptimal. To handle this challenge, we further
propose a temporal diversity-aware news recommendation method that can promote
candidate news that are diverse from recently clicked news, which can help
predict future clicks more accurately. Experiments show that our approach can
consistently improve various news recommendation methods.
