In recent years, the dominant accuracy metric for vector search is the recall
of a result list of fixed size (top-k retrieval), considering as ground truth
the exact vector retrieval results. Although convenient to compute, this metric
is distantly related to the end-to-end accuracy of a full system that
integrates vector search. In this paper we focus on the common case where a
hard decision needs to be taken depending on the vector retrieval results, for
example, deciding whether a query image matches a database image or not. We
solve this as a range search task, where all vectors within a certain radius
from the query are returned.
We show that the value of a range search result can be modeled rigorously
based on the query-to-vector distance. This yields a metric for range search,
RSM, that is both principled and easy to compute without running an end-to-end
evaluation. We apply this metric to the case of image retrieval. We show that
indexing methods that are adapted for top-k retrieval do not necessarily
maximize the RSM. In particular, for inverted file based indexes, we show that
visiting a limited set of clusters and encoding vectors compactly yields near
optimal results.

研究了向量搜索中的召回率指标问题，提出基于范围搜索任务的 RSM 指标，以评估向量搜索的精确性，并证明了适应于 top-k 检索的索引方法不一定能最大化 RSM。

小半径向量搜索

Vector search with small radiuses

This paper presents CLEAR, a retrieval model that seeks to complement
classical lexical exact-match models such as BM25 with semantic matching
signals from a neural embedding matching model. CLEAR explicitly trains the
neural embedding to encode language structures and semantics that lexical
retrieval fails to capture with a novel residual-based embedding learning
method. Empirical evaluations demonstrate the advantages of CLEAR over
state-of-the-art retrieval models, and that it can substantially improve the
end-to-end accuracy and efficiency of reranking pipelines.

本论文提出了 CLEAR 模型，它采用神经嵌入匹配模型的语义匹配信号来补充传统的基于词汇精确匹配的模型，通过一种基于残差的嵌入学习方法，明确地训练嵌入来编码语言结构和语义，实验证明了 CLEAR 在终端到端的准确性和效率等方面相比于最先进的检索模型有显著的优势，并且可以显著地提高重排流水线的吞吐量和精度。