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  4. Cited By
Quantifying Availability and Discovery in Recommender Systems via
  Stochastic Reachability

Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability

30 June 2021
Mihaela Curmei
Sarah Dean
Benjamin Recht
ArXiv (abs)PDFHTML

Papers citing "Quantifying Availability and Discovery in Recommender Systems via Stochastic Reachability"

13 / 13 papers shown
Title
How the Design of YouTube Influences User Sense of Agency
How the Design of YouTube Influences User Sense of Agency
Kai Lukoff
Ulrik Lyngs
Himanshu Zade
J. Liao
James Choi
Kaiyue Fan
Sean A Munson
Alexis Hiniker
43
115
0
28 Jan 2021
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
75
49
0
12 Jan 2021
Do Offline Metrics Predict Online Performance in Recommender Systems?
Do Offline Metrics Predict Online Performance in Recommender Systems?
K. Krauth
Sarah Dean
Alex Zhao
Wenshuo Guo
Mihaela Curmei
Benjamin Recht
Michael I. Jordan
OffRL
61
41
0
07 Nov 2020
A survey of algorithmic recourse: definitions, formulations, solutions,
  and prospects
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi
Gilles Barthe
Bernhard Schölkopf
Isabel Valera
FaML
70
172
0
08 Oct 2020
A Longitudinal Analysis of YouTube's Promotion of Conspiracy Videos
A Longitudinal Analysis of YouTube's Promotion of Conspiracy Videos
Marc Faddoul
Guillaume Chaslot
Hany Farid
43
81
0
06 Mar 2020
A Troubling Analysis of Reproducibility and Progress in Recommender
  Systems Research
A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research
Maurizio Ferrari Dacrema
Simone Boglio
Paolo Cremonesi
Dietmar Jannach
47
198
0
18 Nov 2019
Embarrassingly Shallow Autoencoders for Sparse Data
Embarrassingly Shallow Autoencoders for Sparse Data
Harald Steck
165
257
0
08 May 2019
On the Difficulty of Evaluating Baselines: A Study on Recommender
  Systems
On the Difficulty of Evaluating Baselines: A Study on Recommender Systems
Steffen Rendle
Li Zhang
Y. Koren
73
127
0
04 May 2019
Top-K Off-Policy Correction for a REINFORCE Recommender System
Top-K Off-Policy Correction for a REINFORCE Recommender System
Minmin Chen
Alex Beutel
Paul Covington
Sagar Jain
Francois Belletti
Ed H. Chi
CMLOffRL
117
482
0
06 Dec 2018
Actionable Recourse in Linear Classification
Actionable Recourse in Linear Classification
Berk Ustun
Alexander Spangher
Yang Liu
FaML
121
550
0
18 Sep 2018
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
211
316
0
30 Oct 2017
Boltzmann Exploration Done Right
Boltzmann Exploration Done Right
Nicolò Cesa-Bianchi
Claudio Gentile
Gábor Lugosi
Gergely Neu
100
171
0
29 May 2017
Recommendations as Treatments: Debiasing Learning and Evaluation
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel
Adith Swaminathan
Ashudeep Singh
Navin Chandak
Thorsten Joachims
CML
162
686
0
17 Feb 2016
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