Influenced by the great success of deep learning in computer vision and language
understanding, research in recommendation has shifted to inventing new recommender …

Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but
the unique data processing and evaluation setups used by each work obstruct a full …

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

The most important task in personalized news recommendation is accurate matching
between candidate news and user interest. Most of existing news recommendation methods …

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

With the exponential growth of data volume, information overload has become a problem for
users, and recommender systems were employed to solve this problem. The purpose of the …

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 …

News recommendation systems play a key role in online news reading service. Knowledge
graphs (KG), which contain comprehensive structural knowledge, are well known for their …

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

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