Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application

• URL: http://arxiv.org/abs/2307.14549v1
• Date: Thu, 27 Jul 2023 00:11:59 GMT
• Title: Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking Application
• Authors: Jianjun Yuan and Wei Lee Woon and Ludovik Coba
• Abstract summary: The paper presents an efficient algorithm to solve the sleeping bandit with multiple plays problem in the context of an online recommendation system. The proposed algorithm extends the sleeping bandit algorithm for single arm selection and is guaranteed to achieve theoretical performance with regret upper bounded by $bigO(kN2sqrtTlog T)$.
• Score: 7.0717496233701365
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents an efficient algorithm to solve the sleeping bandit with multiple plays problem in the context of an online recommendation system. The problem involves bounded, adversarial loss and unknown i.i.d. distributions for arm availability. The proposed algorithm extends the sleeping bandit algorithm for single arm selection and is guaranteed to achieve theoretical performance with regret upper bounded by $\bigO(kN^2\sqrt{T\log T})$, where $k$ is the number of arms selected per time step, $N$ is the total number of arms, and $T$ is the time horizon.

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