High recall values are important in benchmarking approximate nearest neighbor (ANN) searches because they measure how well the system retrieves the true closest matches from a dataset. Recall represents the fraction of actual nearest neighbors found by the search algorithm. In applications like recommendation systems, fraud detection, or image retrieval, missing relevant results (low recall) can directly harm user experience or decision accuracy. For example, an e-commerce platform with low recall might fail to show products a user would actually want, leading to lost sales. Benchmarking with high recall ensures the ANN method isn’t just fast but also reliable for real-world use cases where completeness of results matters.
However, achieving high recall often conflicts with speed. Exact nearest neighbor searches guarantee perfect recall but become impractical for large datasets due to their linear time complexity. Vector databases use approximation techniques to reduce latency, but this introduces a trade-off. For instance, algorithms like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) partition data into clusters or build graph structures to limit the search space. These optimizations skip exhaustive comparisons, which speeds up queries but risks missing some true neighbors. Parameters like the number of clusters searched (in IVF) or the graph traversal depth (in HNSW) let developers tune the balance: higher values improve recall but increase compute time.
To manage this trade-off, vector databases often expose knobs like ef (in HNSW) or nprobe (in IVF) that adjust how thoroughly the algorithm explores the search space. For example, increasing ef in HNSW forces the graph traversal to consider more candidate nodes, raising recall at the cost of slower queries. Similarly, a larger nprobe in IVF checks more clusters, improving accuracy but adding overhead. Developers typically benchmark these parameters against their dataset to find an acceptable compromise—like aiming for 90% recall with sub-10ms latency. The choice depends on the application: a medical imaging tool might prioritize recall for diagnostic accuracy, while a real-time chat app could favor speed, accepting minor recall drops.