User interest modeling is critical for personalized news recommendation.
Existing news recommendation methods usually learn a single user embedding for
each user from their previous behaviors to represent their overall interest.
However, user interest is usually diverse and multi-grained, which is difficult
to be accurately modeled by a single user embedding. In this paper, we propose
a news recommendation method with hierarchical user interest modeling, named
HieRec. Instead of a single user embedding, in our method each user is
represented in a hierarchical interest tree to better capture their diverse and
multi-grained interest in news. We use a three-level hierarchy to represent 1)
overall user interest; 2) user interest in coarse-grained topics like sports;
and 3) user interest in fine-grained topics like football. Moreover, we propose
a hierarchical user interest matching framework to match candidate news with
different levels of user interest for more accurate user interest targeting.
Extensive experiments on two real-world datasets validate our method can
effectively improve the performance of user modeling for personalized news
recommendation.

该研究提出了 HieRec，一种基于层次化用户兴趣模型的新闻推荐方法，使用三级层次结构表示用户的兴趣，并使用层次匹配框架实现更准确的用户兴趣目标定位，实验结果表明该方法能够有效改善个性化新闻推荐的用户建模效果。