Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual
improvement among tasks considering their shared knowledge. It is an important topic in …

In light of the emergence of deep reinforcement learning (DRL) in recommender systems
research and several fruitful results in recent years, this survey aims to provide a timely and …

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 …

Recommender systems have been widely applied in different real-life scenarios to help us
find useful information. In particular, reinforcement learning (RL)-based recommender …

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

In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender
System (RS) applications [40]. However, current MTL-based recommendation models tend …

Multi-task learning has been widely applied in computational vision, natural language
processing and other fields, which has achieved well performance. In recent years, a lot of …

Long-term user engagement (LTE) optimization in sequential recommender systems (SRS)
is shown to be suited by reinforcement learning (RL) which finds a policy to maximize long …

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

In Recommender System (RS) applications, reinforcement learning (RL) has recently
emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards …