Paper

Combinatorial Bandits for Incentivizing Agents with Dynamic Preferences

The design of personalized incentives or recommendations to improve user engagement is gaining prominence as digital platform providers continually emerge. We propose a multi-armed bandit framework for matching incentives to users, whose preferences are unknown a priori and evolving dynamically in time, in a resource constrained environment... (read more)

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