Semantic Recall for Vector Search
We introduce Semantic Recall, a novel metric to assess the quality of approximate nearest neighbor search algorithms by considering only semantically relevant objects that are theoretically retrievable via exact nearest neighbor search. Unlike traditional recall, semantic recall does not penalize algorithms for failing to retrieve objects that are semantically irrelevant to the query, even if those objects are among their nearest neighbors. We demonstrate that semantic recall is particularly useful for assessing retrieval quality on queries that have few relevant results among their nearest neighbors-a scenario we uncover to be common within embedding datasets. Additionally, we introduce ...
| Authors | Leonardo Kuffo, Ioanna Tsakalidou, Roberta De Viti et al. |
|---|---|
| Primary category | cs.IR |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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