Tagged “recall”
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The recall-latency curve
Every index parameter moves you along one curve with diminishing returns. How to measure recall honestly, and why distance concentration sets its ceiling.
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Quantization: trading memory for recall
Scalar, product and binary quantization compress vectors by discarding precision. What each one throws away, and why reranking recovers most of it.
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What "approximate" is allowed to mean
ANN has three quite different notions of approximation: a proven ratio, a probability, and no guarantee at all. Which one your index gives you, and why.
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Voronoi cells and the cost of a query near a boundary
A cluster index cuts space into Voronoi cells. Why almost every query lands near an edge, what probing more cells recovers, and why overlap beats probing.
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Hubness: the vectors that turn up in everyone's results
In high dimensions a few points become the nearest neighbour of implausibly many queries. Why the k-NN relation is asymmetric, and what it does to a graph.
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The curse of dimensionality, precisely
High dimensions don't break similarity search by magic. Distance concentration, boundary volume and intrinsic dimension, stated carefully enough to use.