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Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For
  Personalized Email Promo Recommendations

Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations

31 January 2022
A. A. Kocherzhenko
Nirmal Sobha Kartha
Tengfei Li
Hsin-Yi Shih
Shih
Marco Mandic
Mike Fuller
Arshak Navruzyan
ArXivPDFHTML

Papers citing "Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations"

2 / 2 papers shown
Title
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
314
0
30 Oct 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,167
0
06 Jun 2015
1