Burst-induced Multi-Armed Bandit for learning recommendation

Burst-induced Multi-Armed Bandit for learning recommendation

In this paper, we introduce a non-stationary and context-free Multi-Armed Bandit (MAB) problem and a novel algorithm (which we refer to as BMAB) to solve it. The problem is context-free in the sense that no side information about users or items is needed. We work in a continuous-time setting where e...

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書目詳細資料
Main Authors:	ALVES, Rodrigo, LEDENT, Antoine, KLOFT, Marius
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2021
主題:	Recommender Systems Reinforcement Learning Online learning Poisson processes Time Series Analysis bursty methods audience dynamics Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/7209 https://ink.library.smu.edu.sg/context/sis_research/article/8212/viewcontent/3460231.3474250.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Singapore Management University
語言:	English

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