Accurate user interest modeling is important for news recommendation. Most existing
methods for news recommendation rely on implicit feedbacks like click for inferring user …

Both explicit and implicit feedbacks can reflect user opinions on items, which are essential
for learning user preferences in recommendation. However, most current recommendation …

User interest modeling is critical for personalized news recommendation. Existing news
recommendation methods usually learn a single user embedding for each user from their …

News recommendation aims to match news with personalized user interest. Existing
methods for news recommendation usually model user interest from historical clicked news …

Personalized news recommendation methods are widely used in online news services.
These methods usually recommend news based on the matching between news content …

Recommender systems can mitigate the information overload problem by suggesting users'
personalized items. In real-world recommendations such as e-commerce, a typical …

News recommendation calls for deep insights of news articles' underlying semantics.
Therefore, pretrained language models (PLMs), like BERT and RoBERTa, may substantially …

In this paper, we propose a novel Deep Reinforcement Learning framework for news
recommendation. Online personalized news recommendation is a highly challenging …

Precisely recommending candidate news articles to users has always been a core
challenge for personalized news recommendation systems. Most recent works primarily …

News recommendation plays an indispensable role in acquiring daily news for users.
Previous studies make great efforts to model high-order feature interactions between users …