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 …

Planning for both immediate and long-term benefits becomes increasingly important in
recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning …

Large language models (LLM) have demonstrated emergent abilities in text generation,
question answering, and reasoning, facilitating various tasks and domains. Despite their …

The wide popularity of short videos on social media poses new opportunities and
challenges to optimize recommender systems on the video-sharing platforms. Users …

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 …

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 …

Sequential models that encode user activity for next action prediction have become a
popular design choice for building web-scale personalized recommendation systems …

Recommender systems can be characterized as software solutions that provide users with
convenient access to relevant content. Traditionally, recommender systems research …