Optimizing User Engagement Through Adaptive Ad Sequencing
Omid Rafieian
Abstract
This paper develops an offline reinforcement learning framework that identifies and evaluate the ad sequencing policy that optimizes user engagement.
Topics & Concepts
Reinforcement learningComputer scienceUser engagementMachine learningWorld Wide WebData Stream Mining TechniquesMobile Crowdsensing and CrowdsourcingConsumer Market Behavior and Pricing