A recall@10 of 95% means that, for a given query, 95% of all relevant items in a dataset appear within the top 10 results returned by the vector search system. For example, if there are 100 relevant items for a query, the system retrieves 95 of them in the first 10 results. This metric indicates high retrieval effectiveness, as the system captures nearly all relevant items early in the results. However, this interpretation assumes the total number of relevant items per query is small (e.g., ≤10). If there are more relevant items, achieving 95% recall@10 would be impossible, since the top 10 results cannot contain more than 10 items. Thus, the metric is most meaningful in scenarios where the number of relevant items per query is limited, such as recommendation systems targeting a small set of highly relevant options.
To determine if 95% recall@10 is sufficient, users must evaluate their application’s requirements. For example, in e-commerce search, missing 5% of relevant products might be acceptable if the top results still satisfy most users. However, in legal document review or medical diagnosis, missing even 5% of critical information could have serious consequences. Users should also consider precision (the fraction of retrieved items that are relevant). A high recall with low precision could mean the top 10 results include irrelevant items, degrading user experience. Testing with real-world queries and analyzing user feedback or downstream metrics (e.g., conversion rates, task success rates) can clarify whether the trade-off between recall and precision aligns with business goals.
Finally, users should assess computational and latency trade-offs. Achieving 95% recall@10 might require larger vector indexes or more complex algorithms, increasing resource usage. If the application prioritizes speed (e.g., real-time recommendations), a slightly lower recall with faster response times might be preferable. A/B testing can help compare the impact of recall@10 on user behavior, while domain-specific benchmarks (e.g., industry standards for search accuracy) provide additional context. In summary, sufficiency depends on the cost of missing relevant items, the need for speed, and the balance between recall and precision in the user’s unique use case.