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arXiv · 1908.06887

Relevance Proximity Graphs for Fast Relevance Retrieval

Stanislav Morozov·Artem Babenko
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Abstract

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.

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Stanislav Morozov, Artem Babenko. 2019-08-19. Relevance Proximity Graphs for Fast Relevance Retrieval. https://arxiv.org/abs/1908.06887

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