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 …
Contrastive cross-domain recommendation in matching
Cross-domain recommendation (CDR) aims to provide better recommendation results in the
target domain with the help of the source domain, which is widely used and explored in real …
target domain with the help of the source domain, which is widely used and explored in real …
Hierarchical diffusion for offline decision making
Offline reinforcement learning typically introduces a hierarchical structure to solve the long-
horizon problem so as to address its thorny issue of variance accumulation. Problems of …
horizon problem so as to address its thorny issue of variance accumulation. Problems of …
Selective fairness in recommendation via prompts
Recommendation fairness has attracted great attention recently. In real-world systems, users
usually have multiple sensitive attributes (eg age, gender, and occupation), and users may …
usually have multiple sensitive attributes (eg age, gender, and occupation), and users may …
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 …
Adversarial feature translation for multi-domain recommendation
Real-world super platforms such as Google and WeChat usually have different
recommendation scenarios to provide heterogeneous items for users' diverse demands …
recommendation scenarios to provide heterogeneous items for users' diverse demands …
Multi-objective reinforcement learning approach for trip recommendation
Trip recommendation is an intelligent service that provides personalized itinerary plans for
tourists in unfamiliar cities. It aims to construct a series of ordered POIs that maximizes user …
tourists in unfamiliar cities. It aims to construct a series of ordered POIs that maximizes user …
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 …