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 this https URL.

通过生物粒度文档表示法、稀疏嵌入与压缩感知量化等技术，采用基于深度学习的文档表示和 ANN 搜索技术，设计 Progressive Optimization 框架，实现了大规模的 EB 检索，并在百万级和百亿级的数据集上分别实现了高达 +4.3% 到 +17.5% 的提高检出率，同时在一个搜索平台上实现了较大的回报、回忆和 CTR 这些度量方面的提升。