2021
DOI: 10.48550/arxiv.2101.01572
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Sequential Choice Bandits with Feedback for Personalizing users' experience

Anshuka Rangi,
Massimo Franceschetti,
Long Tran-Thanh

Abstract: In this work, we study sequential choice bandits with feedback. We propose bandit algorithms for a platform that personalizes users' experience to maximize its rewards. For each action directed to a given user, the platform is given a positive reward, which is a non-decreasing function of the action, if this action is below the user's threshold. Users are equipped with a patience budget, and actions that are above the threshold decrease the user's patience. When all patience is lost, the user abandons the plat… Show more

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