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17 lines
973 B
TeX
17 lines
973 B
TeX
\subsection{Hash Index}
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To measure the quality of \acrshort{ann} in comparison to \acrshort{knn}, we use $\texttt{recall@k} = |\texttt{ANN\_result} \cap \texttt{KNN\_result}| \div k$.
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This however is not always a good metric, as for two vastly different results, the objectively worse one can score equally well.
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Alternative methods mentioned are \texttt{RDE@k} and \texttt{TDK@k}, but not elaborated any further.
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\subsubsection{Performance vs Recall Trade-off}
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This is a challenging task, primarily focusing on how many vectors we need to visit to achieve a user-specified target recall.
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A few explored strategies are:
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\begin{itemize}
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\item \bi{Uniform autotuning for all queries}: Using learned offline models (e.g. Google CloudSQL VectorAssist)
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\item \bi{Different for each query}: A model predicts the search effort parameters for each query or index
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\item \bi{Adaptive}: Decide to continue or stop early based on the current search state
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\end{itemize}
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