Bandit algorithms: A comprehensive review and their dynamic selection from a portfolio for multicriteria top-k recommendation-Reference-Cited by-同舟云学术

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Bandit algorithms: A comprehensive review and their dynamic selection from a portfolio for multicriteria top-k recommendation

Published:2024-07 Issue: Volume:246 Page:123151
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Letard Alexandre^ORCID,Gutowski Nicolas^ORCID,Camp Olivier^ORCID,Amghar Tassadit

Publisher

Elsevier BV

Reference71 articles.

1. Making contextual decisions with low technical debt;Agarwal,2016

2. Analysis of thompson sampling for the multi-armed bandit problem;Agrawal,2012

3. Further optimal regret bounds for thompson sampling;Agrawal,2013

4. Thompson sampling for contextual bandits with linear payoffs;Agrawal,2013

5. Accurate multi-criteria decision making methodology for recommending machine learning algorithm;Ali;Expert Systems with Applications,2017

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Personalized Dynamic Pricing Based on Improved Thompson Sampling;Mathematics;2024-04-09

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