Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL

TL;DR

This paper introduces CQ-SID and EG-GRPO, a generative retrieval framework for e-commerce search that improves recall efficiency and aligns with ranking goals, demonstrated through offline and online experiments.

Contribution

It proposes a hierarchical semantic encoding method and reinforcement learning approach to enhance generative retrieval's practicality and effectiveness in real-world e-commerce.

Findings

CQ-SID reduces beam search complexity by half.

Offline experiments show up to 26.76% gain in semantic hitrate.

Online A/B tests show GMV increase of 1.15%.

Abstract

Generative retrieval offers a promising alternative by unifying the fragmented multi-stage retrieval process into a single end-to-end model. However, its practical adoption in industrial e-commerce search remains challenging, given the massive and dynamic product catalogs, strict latency requirements, and the need to align retrieval with downstream ranking goals. In this work, we propose a retrieval framework tailored for real-world recall scenarios, positioning generative retrieval as a recall-stage supplement rather than an end-to-end replacement. Our method, CQ-SID (Category-and-Query constrained Semantic ID), employs category-aware and query-item contrastive learning along with Residual Quantized VAEs to encode items into hierarchical semantic cluster identifiers, significantly reducing beam search complexity. Additionally, we develop EG-GRPO (Expert-Guided Group Relative Policy Optimization), a reinforcement learning approach that aligns generative recall with downstream ranking under sparse rewards by injecting ground-truth samples to stabilize training. Offline experiments on TmallAPP search logs show that CQ-SID achieves up to 26.76% and 11.11% relative gains in semantic and personalized click hitrate over RQ-VAE baselines, while halving beam search size. EG-GRPO further improves multi-objective performance. Online A/B tests confirm gains in GMV (+1.15%) and UCTCVR (+0.40%). The generative recall channel now contributes substantially in production, accounting for over 50.25% of exposures, 58.96% of clicks, and 72.63% of purchases, demonstrating a viable path for deploying generative retrieval in real-world e-commerce systems.