News recommendation for anonymous readers is a useful but challenging task
for many news portals, where interactions between readers and articles are
limited within a temporary login session. Previous works tend to formulate
session-based recommendation as a next item prediction task, while they neglect
the implicit feedback from user behaviors, which indicates what users really
like or dislike. Hence, we propose a comprehensive framework to model user
behaviors through positive feedback (i.e., the articles they spend more time
on) and negative feedback (i.e., the articles they choose to skip without
clicking in). Moreover, the framework implicitly models the user using their
session start time, and the article using its initial publishing time, in what
we call "neutral feedback". Empirical evaluation on three real-world news
datasets shows the framework's promising performance of more accurate, diverse
and even unexpectedness recommendations than other state-of-the-art
session-based recommendation approaches.

研究了匿名用户的新闻推荐问题，提出了一种基于正负反馈的用户行为建模框架，综合考虑用户和文章的观看时间等不同反馈信息，以实现更精确、多样化和出乎意料的推荐效果。