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 …
Causal decision transformer for recommender systems via offline reinforcement learning
Reinforcement learning-based recommender systems have recently gained popularity.
However, the design of the reward function, on which the agent relies to optimize its …
However, the design of the reward function, on which the agent relies to optimize its …
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 …
Reinforced moocs concept recommendation in heterogeneous information networks
Massive open online courses (MOOCs), which offer open access and widespread interactive
participation through the internet, are quickly becoming the preferred method for online and …
participation through the internet, are quickly becoming the preferred method for online and …
Knowledge-enhanced causal reinforcement learning model for interactive recommendation
Owing to its inherently dynamic nature and economical training cost, offline reinforcement
learning (RL) is typically employed to implement an interactive recommender system (IRS) …
learning (RL) is typically employed to implement an interactive recommender system (IRS) …
Reinforced KGs reasoning for explainable sequential recommendation
We explore the semantic-rich structured information derived from the knowledge graphs
(KGs) associated with the user-item interactions and aim to reason out the motivations …
(KGs) associated with the user-item interactions and aim to reason out the motivations …
Cipl: Counterfactual interactive policy learning to eliminate popularity bias for online recommendation
Popularity bias, as a long-standing problem in recommender systems (RSs), has been fully
considered and explored for offline recommendation systems in most existing relevant …
considered and explored for offline recommendation systems in most existing relevant …
Locality-sensitive state-guided experience replay optimization for sparse rewards in online recommendation
Online recommendation requires handling rapidly changing user preferences. Deep
reinforcement learning (DRL) is an effective means of capturing users' dynamic interest …
reinforcement learning (DRL) is an effective means of capturing users' dynamic interest …