Partially observable environment estimation with uplift inference for reinforcement learning based recommendation

DOI10.1007/s10994-021-05969-wOpenAlexW3156554472MaRDI QIDQ2071406

Jieping Ye, Yang Yu, Yiping Meng, Zhiwei Qin, Qing-Yang Li, Wenjie Shang

Publication date: 28 January 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-021-05969-w

zbMATH Keywords

reinforcement learninghidden staterecommender systemuplift modelingenvironment estimation

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• XGBoost

Cites Work

• Planning and acting in partially observable stochastic domains
• Handling concept drift via model reuse
• Optimal control of Markov processes with incomplete state information
• The central role of the propensity score in observational studies for causal effects
• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
• Random forests
• Unnamed Item
• Unnamed Item

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