News recommendation is important for online news services. Precise user interest modeling
is critical for personalized news recommendation. Existing news recommendation methods …

Accurate news representation is critical for news recommendation. Most of existing news
representation methods learn news representations only from news texts while ignore the …

News recommendation is of vital importance to alleviating in-formation overload. Recent
research shows that precise modeling of news content and user interests become critical for …

Sequential recommendation (SR) plays an important role in personalized recommender
systems because it captures dynamic and diverse preferences from users' real-time …

An effective online recommendation system should jointly capture users' long-term and short-
term preferences in both users' internal behaviors (from the target recommendation task) …

In heterogeneous e-commerce recommender systems, the type and attribute information of
users and products contain rich semantics, which can benefit the prediction and explanation …

Session-based Recommender Systems (SRSs) have been actively developed to
recommend the next item of an anonymous short item sequence (ie, session). Unlike …

Content-based news recommenders learn words that correlate with user engagement and
recommend articles accordingly. This can be problematic for users with diverse political …

Using AI technology to automatically match Q&A pairs on online health platforms (OHP) can
improve the efficiency of doctor-patient interaction. However, previous methods often …

Heterogeneous information networks (HINs) refer to logical networks involving entities of
multiple types and their multiple relations, which are widely used for modeling real-world …