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Toward Simulating Environments in Reinforcement Learning Based
  Recommendations

Toward Simulating Environments in Reinforcement Learning Based Recommendations

27 June 2019
Xiangyu Zhao
Long Xia
Zhuoye Ding
Dawei Yin
Jiliang Tang
ArXivPDFHTML

Papers citing "Toward Simulating Environments in Reinforcement Learning Based Recommendations"

4 / 4 papers shown
Title
AutoLoss: Automated Loss Function Search in Recommendations
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao
Haochen Liu
Wenqi Fan
Hui Liu
Jiliang Tang
Chong Wang
30
60
0
12 Jun 2021
RecSim NG: Toward Principled Uncertainty Modeling for Recommender
  Ecosystems
RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
Martin Mladenov
Chih-Wei Hsu
Vihan Jain
Eugene Ie
Christopher Colby
Nicolas Mayoraz
H. Pham
Dustin Tran
Ivan Vendrov
Craig Boutilier
BDL
15
31
0
14 Mar 2021
Advances and Challenges in Conversational Recommender Systems: A Survey
Advances and Challenges in Conversational Recommender Systems: A Survey
Chongming Gao
Wenqiang Lei
Xiangnan He
Maarten de Rijke
Tat-Seng Chua
136
273
0
23 Jan 2021
RecSim: A Configurable Simulation Platform for Recommender Systems
RecSim: A Configurable Simulation Platform for Recommender Systems
Eugene Ie
Chih-Wei Hsu
Martin Mladenov
Vihan Jain
Sanmit Narvekar
Jing Wang
Rui Wu
Craig Boutilier
19
177
0
11 Sep 2019
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