RADAR: Recall Augmentation through Deferred Asynchronous Retrieval

M odern large - scale recommender systems employ multi - stage ranking funnel (Retrieval, Pre - ranking, Ranking) to balance engagement and computational constraints (latency, CPU). However, the initial retrieval stage, often relying on efficient but less precise methods like K - Nearest Neighbors (KNN), struggles to effectively surface the most engaging items from billion - scale catalogs, particularly distinguishing highly relevant and engaging candidates from merely relevant ones. We introduce Recall Augmentation through Deferred Asynchronous Retrieval ( RADAR), a novel framework that leverages asynchronous, offline computation to pre - rank a significantly larger candidate set for users using the full complexity ranking model. These top - ranked items are stored and utilized as a high - quality retrieval source during online inference, bypassing online retrieval and pre - ranking stages for these candidates. We demonstrate through offline experiments that RADAR significantly boosts recall ( 2 X Recall @200 vs DNN retrieval baseline) by effectively combining a larger retrieved candidate set with a more powerful ranking model. Online A/B tests confirm a +0.8% lift in topline engagement metrics, validating RADAR as a practical and effective method to improve recommendation quality under strict online serving constraints.