Reinforcement Learning (RL)-based recommender systems (RSs) have garnered
considerable attention due to their ability to learn optimal recommendation policies and …

Recently, short video platforms have achieved rapid user growth by recommending
interesting content to users. The objective of the recommendation is to optimize user …

Current advances in recommender systems have been remarkably successful in optimizing
immediate engagement. However, long-term user engagement, a more desirable …

The contextual bandit has been identified as a powerful framework to formulate the
recommendation process as a sequential decision-making process, where each item is …

In recommender systems, reinforcement learning solutions have shown promising results in
optimizing the interaction sequence between users and the system over the long-term …

Recommendation usually focuses on immediate accuracy metrics like CTR as training
objectives. User retention rate, which reflects the percentage of today's users that will return …

In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to
optimize long-term rewards in recommender systems. Since industrial recommender …

Growing attention has been paid to Reinforcement Learning (RL) algorithms when
optimizing long-term user engagement in sequential recommendation tasks. One challenge …

Current advances in recommender systems have been remarkably successful in optimizing
immediate engagement. However, longterm user engagement, a more desirable …

Short video recommendations often face limitations due to the quality of user feedback,
which may not accurately depict user interests. To tackle this challenge, a new task has …