Short-lived High-volume Bandits

When Thousands of Items Arrive Every Hour: A New Approach to Online Experimentation Researchers have reported a new approach to address a key challenge for online platforms. In a new paper in Operations Research, Jia et al. introduced the Short-lived High-volume Bandits (SLHVB) framework. This models modern platforms where thousands of items—such as ads, stories, or interface designs—arrive each hour but remain available only briefly. The study develops a near-optimal online learning policy that balances exploration and exploitation. Theoretical analysis shows the algorithm achieves nearly the minimal possible loss as the number of user impressions grows. The team tested the policy in a large-scale field experiment with Glance, a leading lock-screen content platform. The policy increased viewing duration by 4.32% and click-through rates by 7.48% compared to the platform’s existing deep-learning-based recommender system.