Large language models (LLMs) are used to generate content for a wide range of tasks, and
are set to reach a growing audience in coming years due to integration in product interfaces …

Some recent news recommendation (NR) methods introduce a Pre-trained Language Model
(PLM) to encode news representation by following the vanilla pre-train and fine-tune …

Personalized news recommendation is important for users to find interesting news
information and alleviate information overload. Although it has been extensively studied …

Personalized news recommendation is an essential technique to help users find interested
news. Accurately matching user's interests and candidate news is the key to news …

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

News recommendation is critical for personalized news access. Most existing news
recommendation methods rely on centralized storage of users' historical news click behavior …

News representation is critical for news recommendation. Most existing methods learn news
representations only from news texts while ignoring the visual information of news. In fact …

News recommendation aims to help online news platform users find their preferred news
articles. Existing news recommendation methods usually learn models from historical user …

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

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