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Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems

Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems

Zhengbang Zhu
Rongjun Qin
Junjie Huang
Xinyi Dai
Yang Yu
Yong Yu
Weinan Zhang
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Papers citing "Understanding or Manipulation: Rethinking Online Performance Gains of Modern Recommender Systems"

3 / 3 papers shown
Title
Measuring Recommender System Effects with Simulated Users
Measuring Recommender System Effects with Simulated Users
Sirui Yao
Yoni Halpern
Nithum Thain
Xuezhi Wang
Kang Lee
Flavien Prost
Ed H. Chi
Jilin Chen
Alex Beutel
48
49
0
12 Jan 2021
A Survey on Neural Network Interpretability
A Survey on Neural Network Interpretability
Yu Zhang
Peter Tiño
A. Leonardis
K. Tang
FaML
XAI
144
660
0
28 Dec 2020
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
169
313
0
30 Oct 2017