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Found 3 papers, 1 papers with code
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System
In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms.
Personalized Bundle Recommendation in Online Games
In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers.
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal.
Model-based Reinforcement Learning
Recommendation Systems
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