RADAR: Recall Augmentation through Deferred Asynchronous Retrieval

• URL: http://arxiv.org/abs/2506.07261v1
• Date: Sun, 08 Jun 2025 19:21:46 GMT
• Title: RADAR: Recall Augmentation through Deferred Asynchronous Retrieval
• Authors: Amit Jaspal, Qian Dang, Ajantha Ramineni,
• Abstract summary: We introduce Recall Augmentation through Deferred Asynchronous Retrieval (RADAR)<n>RADAR pre-ranks a significantly larger candidate set for users using the full complexity ranking model.<n>RADAR significantly boosts recall by effectively combining a larger retrieved candidate set with a more powerful ranking model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern 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 (2X 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.

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