An application might prioritize precision over recall (or vice versa) in vector search depending on the cost of errors and the goal of the system. Precision measures how many retrieved results are relevant (minimizing false positives), while recall measures how many relevant items are retrieved (minimizing false negatives). The choice depends on whether the use case tolerates missing relevant items (favoring precision) or cannot afford to miss them (favoring recall).
Precision is prioritized when false positives are costly. For example, in e-commerce product search, users expect results to closely match their query (e.g., "waterproof hiking boots"). Returning irrelevant items (e.g., non-waterproof boots) could frustrate users and reduce sales. High precision ensures that displayed products are highly relevant, even if some valid options are excluded. Similarly, in voice assistants (e.g., "play jazz music"), precision ensures the first result matches the intent, even if other valid songs are overlooked. Lower recall is acceptable here because users typically interact with top results and won’t scroll through irrelevant options.
Recall is prioritized when missing results has severe consequences. In medical imaging analysis, a vector search system detecting tumors must flag all potential anomalies, even if it means some false positives. Missing a tumor (low recall) could delay critical treatment, while false positives can be reviewed by experts. Similarly, in legal e-discovery, a search for case-related documents must retrieve all potentially relevant files to avoid legal risks, even if lawyers sift through some irrelevant matches. Here, completeness outweighs the cost of extra manual review.
The choice hinges on the domain’s error tolerance. Vector search systems can adjust parameters (e.g., similarity thresholds, k-nearest neighbors) to balance these metrics based on the use case’s requirements.