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Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search
Retrieval pipelines commonly rely on a term-based search to obtain candidate
records, which are subsequently re-ranked. Some candidates are missed by this
approach, e.g., due to a vocabulary mismatch. We address this issue by
replacing the term-based search with a generic k-NN retrieval algorithm, where
a similarity function can take into account subtle term associations. While an
exact brute-force k-NN search using this similarity function is slow, we
demonstrate that an approximate algorithm can be nearly two orders of magnitude
faster at the expense of only a small loss in accuracy. A retrieval pipeline
using an approximate k-NN search can be more effective and efficient than the
term-based pipeline. This opens up new possibilities for designing effective
retrieval pipelines. Our software (including data-generating code) and
derivative data based on the Stack Overflow collection is available online
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