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Recommendation Fairness: From Static to Dynamic

Recommendation Fairness: From Static to Dynamic

5 September 2021
De-Fu Zhang
Jun Wang
    OffRL
ArXivPDFHTML

Papers citing "Recommendation Fairness: From Static to Dynamic"

7 / 7 papers shown
Title
Fairness in Reinforcement Learning: A Survey
Fairness in Reinforcement Learning: A Survey
Anka Reuel
Devin Ma
OffRL
FaML
40
4
0
11 May 2024
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
The Pursuit of Fairness in Artificial Intelligence Models: A Survey
Tahsin Alamgir Kheya
Mohamed Reda Bouadjenek
Sunil Aryal
36
8
0
26 Mar 2024
Achievement and Fragility of Long-term Equitability
Achievement and Fragility of Long-term Equitability
Andrea Simonetto
Ivano Notarnicola
18
1
0
24 Jun 2022
Survey on Fair Reinforcement Learning: Theory and Practice
Survey on Fair Reinforcement Learning: Theory and Practice
Pratik Gajane
A. Saxena
M. Tavakol
George Fletcher
Mykola Pechenizkiy
FaML
OffRL
38
13
0
20 May 2022
User Tampering in Reinforcement Learning Recommender Systems
User Tampering in Reinforcement Learning Recommender Systems
Charles Evans
Atoosa Kasirzadeh
OffRL
AAML
92
39
0
09 Sep 2021
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on
  Open Problems
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Sergey Levine
Aviral Kumar
George Tucker
Justin Fu
OffRL
GP
340
1,960
0
04 May 2020
A Survey on Bias and Fairness in Machine Learning
A Survey on Bias and Fairness in Machine Learning
Ninareh Mehrabi
Fred Morstatter
N. Saxena
Kristina Lerman
Aram Galstyan
SyDa
FaML
326
4,212
0
23 Aug 2019
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