A filter bubble refers to the phenomenon where Internet customization effectively isolates
individuals from diverse opinions or materials, resulting in their exposure to only a select set …

Offline reinforcement learning (RL), a technology that offline learns a policy from logged data
without the need to interact with online environments, has become a favorable choice in …

Recommendation systems are popular in many domains. Researchers usually focus on the
effectiveness of recommendation (eg, precision) but neglect the popularity bias that may …

In a recommender system, a user's interaction is often biased by the items' displaying
positions and popularity, as well as the user's self-selection. Most existing recommendation …

In the information-overloaded era of the Web, recommender systems that provide
personalized content filtering are now the mainstream portal for users to access Web …

Providing recommendations that are both relevant and diverse is a key consideration of
modern recommender systems. Optimizing both of these measures presents a fundamental …

AI recommendations influence our daily decisions. The convenience of navigating
personalized content goes hand-in-hand with the notorious filter bubble effect, which may …

Recommender systems often struggle to strike a balance between matching users' tastes
and providing unexpected recommendations. When recommendations are too narrow and …

From e-commerce to music and news, recommender systems are tailored to specific
scenarios. While researching generic models applicable to various scenarios is crucial …

Understanding high energy density physics (HEDP) is critical for advancements in fusion
energy and astrophysics. The computational demands of the computer models used for …