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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 comprehensive overview of recent trends of deep reinforcement learning in recommender systems. We start by motivating the application of DRL in recommender systems, followed by a taxonomy of current DRL-based recommender systems and a summary of existing methods. We discuss emerging topics, open issues, and provide our perspective on advancing the domain. The survey serves as introductory material for readers from academia and industry to the topic and identifies notable opportunities for further research. [ABSTRACT FROM AUTHOR]

Precise prescription of medication dosing is crucial to patients, especially among Intensive Care Unit (ICU) patients. However, improper administration of some sensitive therapeutic medications (e.g., heparin) might place patients at unneeded risk, even life-threatening. Numerous factors such as a patient's clinical phenotype, genotype, and environmental factors will affect the heparin dose response. As a result, it is challenging to prescribe the optimal initial dose of heparin. In this paper, an individualized dosing policy is proposed to determine the optimal initial dose and minimize the risk of mis-dosing, as well as preventing the patients from late complications associated with medications dosing. A latent batch-constrained deep reinforcement learning (RL) algorithm is proposed to guarantee the safety of the medication recommendation system. The agent can observe a latent representation of patents and generate medication dosing solutions in successive and limited action spaces. The individualized dosing policy aims to reduce the extrapolation errors in the off-policy algorithms, by evaluating over-dosing and under-dosing of heparin in patients. Our results evaluated on Medical Information Mart for Intensive Care III (MIMIC-III) database demonstrate that the latent batch-constrained RL algorithm can work effectively from the retrospective data, showing promise to be used in future medication dosing policies. [ABSTRACT FROM AUTHOR]

Knowledge graphs (KGs) can provide rich, structured information for recommendation systems as well as increase accuracy and perform explicit reasoning. Deep reinforcement learning (RL) has also sparked great interest in personalized recommendations. The combination of the two holds promise in carrying out interpretable causal inference procedures and improving the performance of graph-structured recommendation. However, most KG-based recommendation focus on rich semantic relationships between entities in a heterogeneous knowledge graph, and thus fail to fully make use of the image information corresponding to an entity. In order to address these issues, we proposed a novel Multi-modal Knowledge-aware Reinforcement Learning Network (MKRLN), which couples recommendation and interpretability by providing actual paths in multi-modal KG (MKG). The MKRLN can generate path representation by composing the structural and visual information of entities, and infers the underlying rational of agent-MKG interactions by leveraging the sequential dependencies within a path from the MKG. In addition, as KGs have too many attributes and entities, their combination with RL leads to too many action spaces and states in the reinforcement learning space, which complicates the search of action spaces. Furthermore, in order to solve this problem, we proposed a new hierarchical attention-path, which makes users focus their attention on the items they are interested in. This reduces the relations and entities in the KGs, which in turn reduces the action space and state in RL, shortens the path to the target entity, and improves the accuracy of recommendation. Our model has explicit explanation ability in knowledge and images. Finally, we extensively evaluated our model on several large-scale real-world benchmark datasets, and it yielded favorable results compared with state-of-the-art methods. [ABSTRACT FROM AUTHOR]