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 …
Comprehensive review of deep reinforcement learning methods and applications in economics
The popularity of deep reinforcement learning (DRL) applications in economics has
increased exponentially. DRL, through a wide range of capabilities from reinforcement …
increased exponentially. DRL, through a wide range of capabilities from reinforcement …
[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 …
CIRS: Bursting filter bubbles by counterfactual interactive recommender system
While personalization increases the utility of recommender systems, it also brings the issue
of filter bubbles. eg, if the system keeps exposing and recommending the items that the user …
of filter bubbles. eg, if the system keeps exposing and recommending the items that the user …
Off-policy actor-critic for recommender systems
Industrial recommendation platforms are increasingly concerned with how to make
recommendations that cause users to enjoy their long term experience on the platform …
recommendations that cause users to enjoy their long term experience on the platform …
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 …
Adaptive course recommendation in MOOCs
In the process of course learning, users incline to change their interests with the
improvements of their cognition. Existing course recommendation methods usually assume …
improvements of their cognition. Existing course recommendation methods usually assume …
" Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin …
Search, recommendation, and online advertising are the three most important information-
providing mechanisms on the web. These information seeking techniques, satisfying users' …
providing mechanisms on the web. These information seeking techniques, satisfying users' …
Values of user exploration in recommender systems
Reinforcement Learning (RL) has been sought after to bring next-generation recommender
systems to further improve user experience on recommendation platforms. While the …
systems to further improve user experience on recommendation platforms. While the …
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 …