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 reinforcement learning with the confusion-matrix-based dynamic reward function for customer credit scoring
Y Wang, Y Jia, Y Tian, J Xiao - Expert Systems with Applications, 2022 - Elsevier
Customer credit scoring is a dynamic interactive process. Simply designing the static reward
function for deep reinforcement learning may be difficult to guide an agent to adapt to the …
function for deep reinforcement learning may be difficult to guide an agent to adapt to the …
Mobile robot path planning using a QAPF learning algorithm for known and unknown environments
This paper presents the computation of feasible paths for mobile robots in known and
unknown environments using a QAPF learning algorithm. Q-learning is a reinforcement …
unknown environments using a QAPF learning algorithm. Q-learning is a reinforcement …
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 …
Multi-task recommendations with reinforcement learning
In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender
System (RS) applications [40]. However, current MTL-based recommendation models tend …
System (RS) applications [40]. However, current MTL-based recommendation models tend …
DCFGAN: An adversarial deep reinforcement learning framework with improved negative sampling for session-based recommender systems
In recent years, with the development of Internet technology, recommender systems have
been widely used by virtue of their ability to meet the personalized needs of users. In order …
been widely used by virtue of their ability to meet the personalized needs of users. In order …
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 …
Exploration and regularization of the latent action space in recommendation
In recommender systems, reinforcement learning solutions have effectively boosted
recommendation performance because of their ability to capture long-term user-system …
recommendation performance because of their ability to capture long-term user-system …