MoR: An Adaptive Retrieval Allocation Balancing Long-Term Interest and Short-Term Evidence

Abstract

Modern recommender systems often rely on multiple retrieval sources to generate candidate items, yet determining optimal quota allocation across these sources remains challenging. We present Mixture of Retrieval (MoR) bandit, a novel framework that dynamically optimizes retrieval allocation by balancing long-term user interests with short-term behavioral signals. Unlike traditional static approaches, our method employs a modified Thompson Sampling algorithm that combines a user’s affinity scores to third-party video channels with their recent behavioral signals, using exponential decay to prevent over-reliance on stale signals. The framework enhances personalization while promoting exploration of potentially valuable but under-exposed content channels. In a large-scale A/B test on Prime Video, MoR bandit achieved a significant 4% increase in third-party channel subscriptions and substantial improvements in user engagement metrics. At the time of writing this paper, MoR has been fully launched in production worldwide serving hundreds of millions of customers. Our approach provides a generalizable solution for multi-source retrieval optimization in large-scale recommender systems.