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News recommender systems are essential for helping users to efficiently andeffectively find out those interesting news from a large amount of news. Mostof existing news recommender systems usually learn topic-level representationsof users and news for recommendation, and neglect to learn more informativeaspect-level features of users and news for more accurate recommendation. As aresult, they achieve limited recommendation performance. Aiming at addressingthis 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 ofrelated words, which indicates specific aspects described by the news. In ANRS,news aspect-level encoder and user aspect-level encoder are devised to learnthe fine-grained aspect-level representations of user's preferences and newscharacteristics respectively, which are fed into click predictor to judge theprobability of the user clicking the candidate news. Extensive experiments aredone on the commonly used real-world dataset MIND, which demonstrate thesuperiority of our method compared with representative and state-of-the-artmethods.

Aspect-driven User Preference and News Representation Learning for News Recommendation