  News recommender systems are essential for helping users to efficiently and
effectively find out those interesting news from a large amount of news. Most
of existing news recommender systems usually learn topic-level representations
of users and news for recommendation, and neglect to learn more informative
aspect-level features of users and news for more accurate recommendation. As a
result, they achieve limited recommendation performance. Aiming at addressing
this deficiency, we propose a novel Aspect-driven News Recommender System
(ANRS) built on aspect-level user preference and news representation learning.
Here, news aspect is fine-grained semantic information expressed by a set of
related words, which indicates specific aspects described by the news. In ANRS,
news aspect-level encoder and user aspect-level encoder are devised to learn
the fine-grained aspect-level representations of user's preferences and news
characteristics respectively, which are fed into click predictor to judge the
probability of the user clicking the candidate news. Extensive experiments are
done on the commonly used real-world dataset MIND, which demonstrate the
superiority of our method compared with representative and state-of-the-art
methods.
