Accurate user modeling is critical for news recommendation. Existing news recommendation
methods usually model users' interest from their behaviors via sequential or attentive …

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 …

As an important way to alleviate information overload, a recommender system aims to filter
out irrelevant information for users and provides them items that they may be interested in. In …

Sequential recommender systems aim to predict users' next interested item given their
historical interactions. However, a long-standing issue is how to distinguish between users' …

Graph neural networks (GNNs), with their promising potential to learn effective graph
representation, have been widely used for recommender systems, in which the given graph …

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

The exponential outbreak of news articles makes it troublesome for the readers to find,
select and read the most relevant ones and alleviate the resulting information and cognitive …

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 is an important task for online news platforms to target
user interests and alleviate information overload. Most existing methods leverage news …

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