  News recommendation is important for online news services. Precise user
interest modeling is critical for personalized news recommendation. Existing
news recommendation methods usually rely on the implicit feedback of users like
news clicks to model user interest. However, news click may not necessarily
reflect user interests because users may click a news due to the attraction of
its title but feel disappointed at its content. The dwell time of news reading
is an important clue for user interest modeling, since short reading dwell time
usually indicates low and even negative interest. Thus, incorporating the
negative feedback inferred from the dwell time of news reading can improve the
quality of user modeling. In this paper, we propose a neural news
recommendation approach which can incorporate the implicit negative user
feedback. We propose to distinguish positive and negative news clicks according
to their reading dwell time, and respectively learn user representations from
positive and negative news clicks via a combination of Transformer and additive
attention network. In addition, we propose to compute a positive click score
and a negative click score based on the relevance between candidate news
representations and the user representations learned from the positive and
negative news clicks. The final click score is a combination of positive and
negative click scores. Besides, we propose an interactive news modeling method
to consider the relatedness between title and body in news modeling. Extensive
experiments on real-world dataset validate that our approach can achieve more
accurate user interest modeling for news recommendation.

Information Retrieval