Burst-induced Multi-Armed Bandit for learning recommendation

• Main Authors: ALVES, Rodrigo, LEDENT, Antoine, KLOFT, Marius
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Recommender Systems; Reinforcement Learning; Online learning; Poisson processes; Time Series Analysis; bursty methods; audience dynamics; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/7209; https://ink.library.smu.edu.sg/context/sis_research/article/8212/viewcontent/3460231.3474250.pdf
• Institution: Singapore Management University

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...