  Given a vector dataset $\mathcal{X}$, a query vector $\vec{x}_q$, graph-based
Approximate Nearest Neighbor Search (ANNS) aims to build a proximity graph (PG)
as an index of $\mathcal{X}$ and approximately return vectors with minimum
distances to $\vec{x}_q$ by searching over the PG index. It suffers from the
large-scale $\mathcal{X}$ because a PG with full vectors is too large to fit
into the memory, e.g., a billion-scale $\mathcal{X}$ in 128 dimensions would
consume nearly 600 GB memory. To solve this, Product Quantization (PQ)
integrated graph-based ANNS is proposed to reduce the memory usage, using
smaller compact codes of quantized vectors in memory instead of the large
original vectors. Existing PQ methods do not consider the important routing
features of PG, resulting in low-quality quantized vectors that affect the
ANNS's effectiveness. In this paper, we present an end-to-end Routing-guided
learned Product Quantization (RPQ) for graph-based ANNS. It consists of (1) a
\textit{differentiable quantizer} used to make the standard discrete PQ
differentiable to suit for back-propagation of end-to-end learning, (2) a
\textit{sampling-based feature extractor} used to extract neighborhood and
routing features of a PG, and (3) a \textit{multi-feature joint training
module} with two types of feature-aware losses to continuously optimize the
differentiable quantizer. As a result, the inherent features of a PG would be
embedded into the learned PQ, generating high-quality quantized vectors.
Moreover, we integrate our RPQ with the state-of-the-art DiskANN and existing
popular PGs to improve their performance. Comprehensive experiments on
real-world large-scale datasets (from 1M to 1B) demonstrate RPQ's superiority,
e.g., 1.7$\times$-4.2$\times$ improvement on QPS at the same recall@10 of 95\%.
