Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval

TL;DR

This paper introduces a bi-granular document representation framework that combines sparse and dense embeddings, optimized progressively for scalable embedding-based retrieval, significantly improving accuracy and efficiency in large-scale search applications.

Contribution

The paper proposes a novel progressive optimization framework for bi-granular embeddings, enabling scalable and accurate retrieval in massive corpora with reduced memory footprint.

Findings

Up to +4.3% recall on million-scale corpus

Up to +17.5% recall on billion-scale corpus

Substantial gains in revenue, recall, and CTR in a real search platform

Abstract

Ad-hoc search calls for the selection of appropriate answers from a massive-scale corpus. Nowadays, the embedding-based retrieval (EBR) becomes a promising solution, where deep learning based document representation and ANN search techniques are allied to handle this task. However, a major challenge is that the ANN index can be too large to fit into memory, given the considerable size of answer corpus. In this work, we tackle this problem with Bi-Granular Document Representation, where the lightweight sparse embeddings are indexed and standby in memory for coarse-grained candidate search, and the heavyweight dense embeddings are hosted in disk for fine-grained post verification. For the best of retrieval accuracy, a Progressive Optimization framework is designed. The sparse embeddings are learned ahead for high-quality search of candidates. Conditioned on the candidate distribution induced by the sparse embeddings, the dense embeddings are continuously learned to optimize the discrimination of ground-truth from the shortlisted candidates. Besides, two techniques: the contrastive quantization and the locality-centric sampling are introduced for the learning of sparse and dense embeddings, which substantially contribute to their performances. Thanks to the above features, our method effectively handles massive-scale EBR with strong advantages in accuracy: with up to +4.3% recall gain on million-scale corpus, and up to +17.5% recall gain on billion-scale corpus. Besides, Our method is applied to a major sponsored search platform with substantial gains on revenue (+1.95%), Recall (+1.01%) and CTR (+0.49%). Our code is available at https://github.com/microsoft/BiDR.