News recommendation (NR) is essential for online news services. Existing NR methods
typically adopt a news-user representation learning framework, facing two potential …

News recommendation models extremely rely on the interactive information between users
and news articles to personalize the recommendation. Therefore, one of their most serious …

Existing news recommendation methods suffer from sparse and weak interaction data,
leading to reduced effectiveness and explainability. Knowledge reasoning, which explores …

In the news recommendation, users are overwhelmed by thousands of news daily, which
makes the users' behavior data have high sparsity. Therefore, only considering a single …

The widespread use of temporal aspects in user modeling indicates their importance, and
their consideration showed to be highly effective in various domains related to user …

Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product
search. However, existing CTR methods still struggle in the product search settings due to …

Leveraging the side information associated with entities (ie users and items) to enhance the
performance of recommendation systems has been widely recognized as an important …

The news recommender systems are marked by a few unique challenges specific to the
news domain. These challenges emerge from rapidly evolving readers' interests over …

With the integration of different fields, a large amount of heterogeneous data converges into
a heterogeneous network with different types of nodes and edges. Then it is published by …

Intelligent human-device interfaces play key roles in fully automated vehicles (FAVs),
ensuring smooth interactions and improving the driving experience. Listening to news is a …