A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits-Reference-Cited by-同舟云学术

A Field Test of Bandit Algorithms for Recommendations: Understanding the Validity of Assumptions on Human Preferences in Multi-armed Bandits

Published:2023-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
language:
Short-container-title:

Author:

Leqi Liu¹^ORCID,Zhou Giulio¹^ORCID,Kilinc-Karzan Fatma¹^ORCID,Lipton Zachary²^ORCID,Montgomery Alan¹^ORCID

Affiliation:

1. Carnegie Mellon University, United States

2. Machine Learning Department and Tepper School of Business, Carnegie Mellon University, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3544548.3580670

Reference61 articles.

1. Hervé Abdi . 2010. Holm’s sequential Bonferroni procedure. Encyclopedia of research design 1, 8 ( 2010 ), 1–8. Hervé Abdi. 2010. Holm’s sequential Bonferroni procedure. Encyclopedia of research design 1, 8 (2010), 1–8.

2. Daniel Acuna and Paul Schrater . 2008 . Bayesian modeling of human sequential decision-making on the multi-armed bandit problem . In Proceedings of the 30th annual conference of the cognitive science society, Vol. 100 . Washington, DC: Cognitive Science Society, 200–300. Daniel Acuna and Paul Schrater. 2008. Bayesian modeling of human sequential decision-making on the multi-armed bandit problem. In Proceedings of the 30th annual conference of the cognitive science society, Vol. 100. Washington, DC: Cognitive Science Society, 200–300.

3. Peter Auer , Nicolo Cesa-Bianchi , and Paul Fischer . 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning 47, 2 ( 2002 ), 235–256. Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning 47, 2 (2002), 235–256.

4. Adaptive Preference

5. Introduction to Bandits in Recommender Systems