  In plenty of machine learning applications, the most relevant items for a
particular query should be efficiently extracted, while the relevance function
is based on a highly-nonlinear model, e.g., DNNs or GBDTs. Due to the high
computational complexity of such models, exhaustive search is infeasible even
for medium-scale problems. To address this issue, we introduce Relevance
Proximity Graphs (RPG): an efficient non-exhaustive approach that provides a
high-quality approximate solution for maximal relevance retrieval. Namely, we
extend the recent similarity graphs framework to the setting, when there is no
similarity measure defined on item pairs, which is a common practical use-case.
By design, our approach directly maximizes off-the-shelf relevance functions
and does not require any proxy auxiliary models. Via extensive experiments, we
show that the developed method provides excellent retrieval accuracy while
requiring only a few model computations, outperforming indirect models. We
open-source our implementation as well as two large-scale datasets to support
further research on relevance retrieval.
