Reinforcement learning based recommender systems: A survey
Recommender systems (RSs) have become an inseparable part of our everyday lives. They
help us find our favorite items to purchase, our friends on social networks, and our favorite …
help us find our favorite items to purchase, our friends on social networks, and our favorite …
A survey of deep reinforcement learning in recommender systems: A systematic review and future directions
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 …
research and several fruitful results in recent years, this survey aims to provide a timely and …
[HTML][HTML] Deep reinforcement learning in recommender systems: A survey and new perspectives
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 …
research and several fruitful results in recent years, this survey aims to provide a timely and …
Deep learning for recommender systems: A Netflix case study
Deep learning has profoundly impacted many areas of machine learning. However, it took a
while for its impact to be felt in the field of recommender systems. In this article, we outline …
while for its impact to be felt in the field of recommender systems. In this article, we outline …
A survey on reinforcement learning for recommender systems
Recommender systems have been widely applied in different real-life scenarios to help us
find useful information. In particular, reinforcement learning (RL)-based recommender …
find useful information. In particular, reinforcement learning (RL)-based recommender …
A bird's-eye view of reranking: from list level to page level
Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to
maximize the total utility. With the development of multimedia and user interface design, the …
maximize the total utility. With the development of multimedia and user interface design, the …
Smart E-learning framework for personalized adaptive learning and sequential path recommendations using reinforcement learning
Learning activities are considerably supported and improved by the rapid advancement of e-
learning systems. This gives students a tremendous chance to participate in learning …
learning systems. This gives students a tremendous chance to participate in learning …
Multi-agent reinforcement learning: Methods, applications, visionary prospects, and challenges
Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI)
technique. However, current studies and applications need to address its scalability, non …
technique. However, current studies and applications need to address its scalability, non …
Hierarchical reinforcement learning with dynamic recurrent mechanism for course recommendation
In online learning scenarios, the learners usually hope to find courses that meet their
preferences and the needs for their future developments. Thus, there is a great need to …
preferences and the needs for their future developments. Thus, there is a great need to …
On the opportunities and challenges of offline reinforcement learning for recommender systems
Reinforcement learning serves as a potent tool for modeling dynamic user interests within
recommender systems, garnering increasing research attention of late. However, a …
recommender systems, garnering increasing research attention of late. However, a …