TV program recommendation is very important for users to find interesting TV programs and
avoid confusing users with a lot of information. Currently, they are basically traditional …

The boom in the field of movies and TV programs, which is a kind of information overload,
may lead to poor user experience and are detrimental to the healthy development of the …

News consumption has shifted over time from traditional media to online platforms, which
use recommendation algorithms to help users navigate through the large incoming streams …

Matching candidate news with user interests is critical for news recommendation. Current
studies on news recommendation mainly model a single user interest embedding from the …

Accurate matching between user and candidate news plays a fundamental role in news
recommendation. Most existing studies capture fine-grained user interests through effective …

During graduate education, postgraduates have to spend considerable time finding papers
to explore the development branches of their field. However, existing paper …

Single-cell RNA sequencing (scRNA-seq) methods based on graph neural networks (GNNs)
can make good use of cell relation graphs. Considering the cell relation graph is unknown in …

Personalized news recommendation aims to recommend candidate news to the target user,
according to the clicked news history. The user-news interaction data exhibits power-law …

Nowadays, news spreads faster than it is consumed. This, alongside the rapid news cycle
and delayed updates, has led to a challenging news cold-start issue. Likewise, the user cold …

Personalised news recommendation comprises two crucial components: news
understanding and user modelling. Previous studies have attempted to model news …