Recall@1 vs. Recall@100 Recall@1 measures whether the top result returned by a system is relevant (i.e., a "hit"). For example, in a search for "best running shoes," if the first result is a relevant product, Recall@1 is 100%. Recall@100, however, checks if any of the top 100 results contain a relevant item. A high Recall@1 indicates the system excels at ranking the most relevant item first, while a high Recall@100 suggests it can surface relevant items somewhere in a larger set, even if not at the top. If a system has high Recall@100 but low Recall@1, it implies the model struggles to prioritize the best result but can still retrieve relevant candidates when allowed more attempts. This is common in systems that trade off ranking precision for broader coverage.
Precision@1 vs. Precision@10 Precision@1 evaluates whether the first result is relevant (e.g., 100% if correct, 0% otherwise). Precision@10 calculates the fraction of relevant items in the top 10 results. A high Precision@1 means the system reliably returns a correct result in the top position, which is critical for applications like voice assistants or single-result searches. Precision@10, however, reflects how well the system maintains relevance across a larger set. For instance, a recommendation system with 8 relevant items in the top 10 has Precision@10 of 80%. If Precision@1 is high but Precision@10 drops significantly, it suggests the system prioritizes the first result’s quality at the expense of later results, potentially due to overfitting or limited diversity in retrieval.
What These Metrics Reveal High Recall@1 and Precision@1 indicate a system optimized for accuracy in the top position, ideal for user-facing scenarios where only the first result matters. However, high Recall@100 with lower Precision@10 might signal a system designed for recall-heavy tasks (e.g., legal document retrieval), where finding all possible matches matters more than ranking order. Conversely, a system with high Precision@10 but lower Recall@100 could prioritize minimizing irrelevant results, even at the cost of missing some relevant ones. These differences highlight trade-offs: ranking precision vs. coverage, single-result reliability vs. broad relevance, and the system’s alignment with specific use cases (e.g., search engines vs. exploratory recommendation systems).