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 …
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 …
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) …
[HTML][HTML] Economic recommender systems–a systematic review
Many of today's online services provide personalized recommendations to their users. Such
recommendations are typically designed to serve certain user needs, eg, to quickly find …
recommendations are typically designed to serve certain user needs, eg, to quickly find …
Multi-actor mechanism for actor-critic reinforcement learning
L Li, Y Li, W Wei, Y Zhang, J Liang - Information Sciences, 2023 - Elsevier
Value estimation is a critical problem in Value-Based reinforcement learning. Most related
studies focus on using multi-critic to reduce estimation bias and seldom consider the multi …
studies focus on using multi-critic to reduce estimation bias and seldom consider the multi …
Latent semantic indexing-based hybrid collaborative filtering for recommender systems
F Horasan - Arabian Journal for Science and Engineering, 2022 - Springer
Advances in information technologies increase the number and diversity of digital objects.
This increase poses significant problems in reaching the target audience of digital products …
This increase poses significant problems in reaching the target audience of digital products …
CDCM: ChatGPT-Aided Diversity-Aware Causal Model for Interactive Recommendation
X Wen, W Nie, J Liu, Y Su, Y Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In recent years, interactive recommender systems (IRSs) have attracted extensive interest.
Existing IRSs are typically implemented with offline reinforcement learning (RL). They are …
Existing IRSs are typically implemented with offline reinforcement learning (RL). They are …
Top-aware recommender distillation with deep reinforcement learning
Most existing recommenders focus on providing users with a list of recommended products.
In practical, users may only pay attention to the recommendations at the top positions. Our …
In practical, users may only pay attention to the recommendations at the top positions. Our …