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Performative Recommendation: Diversifying Content via Strategic
  Incentives

Performative Recommendation: Diversifying Content via Strategic Incentives

8 February 2023
Itay Eilat
Nir Rosenfeld
ArXivPDFHTML

Papers citing "Performative Recommendation: Diversifying Content via Strategic Incentives"

9 / 9 papers shown
Title
Machine Learning Should Maximize Welfare, Not (Only) Accuracy
Machine Learning Should Maximize Welfare, Not (Only) Accuracy
Nir Rosenfeld
Haifeng Xu
HAI
FaML
71
1
0
17 Feb 2025
Performative Prediction on Games and Mechanism Design
Performative Prediction on Games and Mechanism Design
António Góis
Mehrnaz Mofakhami
Fernando P. Santos
Simon Lacoste-Julien
Gauthier Gidel
26
0
0
09 Aug 2024
Performative Debias with Fair-exposure Optimization Driven by Strategic
  Agents in Recommender Systems
Performative Debias with Fair-exposure Optimization Driven by Strategic Agents in Recommender Systems
Zhichen Xiang
Hongke Zhao
Chuang Zhao
Ming He
Jianping Fan
19
1
0
25 Jun 2024
Accounting for AI and Users Shaping One Another: The Role of
  Mathematical Models
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
Sarah Dean
Evan Dong
Meena Jagadeesan
Liu Leqi
35
6
0
18 Apr 2024
Incentivizing High-Quality Content in Online Recommender Systems
Incentivizing High-Quality Content in Online Recommender Systems
Xinyan Hu
Meena Jagadeesan
Michael I. Jordan
Jacob Steinhard
25
10
0
13 Jun 2023
Information Discrepancy in Strategic Learning
Information Discrepancy in Strategic Learning
Yahav Bechavod
Chara Podimata
Zhiwei Steven Wu
Juba Ziani
28
45
0
01 Mar 2021
Outside the Echo Chamber: Optimizing the Performative Risk
Outside the Echo Chamber: Optimizing the Performative Risk
John Miller
Juan C. Perdomo
Tijana Zrnic
71
93
0
17 Feb 2021
How to Learn when Data Reacts to Your Model: Performative Gradient
  Descent
How to Learn when Data Reacts to Your Model: Performative Gradient Descent
Zachary Izzo
Lexing Ying
James Y. Zou
79
72
0
15 Feb 2021
How Algorithmic Confounding in Recommendation Systems Increases
  Homogeneity and Decreases Utility
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
A. Chaney
Brandon M Stewart
Barbara E. Engelhardt
CML
161
312
0
30 Oct 2017
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