  News recommendation aims to match news with personalized user interest.
Existing methods for news recommendation usually model user interest from
historical clicked news without the consideration of candidate news. However,
each user usually has multiple interests, and it is difficult for these methods
to accurately match a candidate news with a specific user interest. In this
paper, we present a candidate-aware user modeling method for personalized news
recommendation, which can incorporate candidate news into user modeling for
better matching between candidate news and user interest. We propose a
candidate-aware self-attention network that uses candidate news as clue to
model candidate-aware global user interest. In addition, we propose a
candidate-aware CNN network to incorporate candidate news into local behavior
context modeling and learn candidate-aware short-term user interest. Besides,
we use a candidate-aware attention network to aggregate previously clicked news
weighted by their relevance with candidate news to build candidate-aware user
representation. Experiments on real-world datasets show the effectiveness of
our method in improving news recommendation performance.
