Synthetic Data Powers Product Retrieval for Long-tail Knowledge-Intensive Queries in E-commerce Search

Dan Ou; Dongshuai Li; Fuyu Lv; Gui Ling; Haihong Tang; Weiyuan Li; Wenjun Peng; Xingxian Liu; Yue Jiang

arxiv: 2602.23620 · v2 · submitted 2026-02-27 · 💻 cs.IR

Synthetic Data Powers Product Retrieval for Long-tail Knowledge-Intensive Queries in E-commerce Search

Gui Ling , Weiyuan Li , Yue Jiang , Wenjun Peng , Xingxian Liu , Dongshuai Li , Fuyu Lv , Dan Ou

show 1 more author

Haihong Tang

This is my paper

Pith reviewed 2026-05-15 19:25 UTC · model grok-4.3

classification 💻 cs.IR

keywords synthetic dataproduct retrievallong-tail queriese-commerce searchquery rewritingLLM distillationknowledge-intensive queriesoffline pipeline

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The pith

Synthetic query-product pairs from LLM rewriting improve retrieval for long-tail knowledge-intensive e-commerce queries.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that long-tail queries in e-commerce search, which lack behavioral logs and require domain knowledge, can be handled by generating synthetic training data. An LLM-based multi-candidate rewriting model produces rewritten queries, which an offline retrieval pipeline then pairs with relevant products using multiple reward signals. Adding these pairs to standard retrieval model training yields significant gains in candidate recall. Online human side-by-side tests confirm better user experience. The method works by distilling offline LLM strengths into efficient online retrieval without further modifications.

Core claim

The central claim is that an efficient data synthesis framework can distill the query-rewriting ability of a powerful offline model into well-curated synthetic query-product pairs; these pairs, when incorporated into retrieval model training, produce substantial improvements on long-tail knowledge-intensive queries while avoiding distributional shift that would otherwise reduce recall or add irrelevant items.

What carries the argument

Multi-candidate query rewriting model trained with multiple reward signals whose outputs are turned into query-product pairs by a powerful offline retrieval pipeline.

If this is right

Retrieval models achieve higher recall on queries that previously lacked sufficient training examples.
The same synthetic data can be reused across multiple retrieval architectures without architecture-specific changes.
Online user experience improves as measured by side-by-side human judgments of search result quality.
The approach reduces dependence on scarce real behavioral logs for long-tail query handling.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same distillation pattern could transfer to other retrieval settings that suffer from long-tail query sparsity, such as specialized web or enterprise search.
If the offline pipeline can be run at larger scale, the volume of synthetic pairs might further close the gap between head and tail query performance.
Combining this synthesis step with existing ranking stages could create a fully data-driven pipeline that needs fewer hand-crafted features for knowledge-intensive queries.

Load-bearing premise

The distribution of the generated rewritten queries stays close enough to real queries that the synthetic pairs increase recall without adding irrelevant products.

What would settle it

No measurable lift in offline recall metrics or online side-by-side human ratings after the synthetic pairs are added to retrieval training would falsify the claim.

Figures

Figures reproduced from arXiv: 2602.23620 by Dan Ou, Dongshuai Li, Fuyu Lv, Gui Ling, Haihong Tang, Weiyuan Li, Wenjun Peng, Xingxian Liu, Yue Jiang.

**Figure 1.** Figure 1: Our proposed data synthesis framework targeting long-tail knowledge-intensive queries. The framework comprises [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 2.** Figure 2: A multi-reward design for rewriting model opti [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

read the original abstract

Product retrieval is the backbone of e-commerce search: for each user query, it identifies a high-recall candidate set from billions of items, laying the foundation for high-quality ranking and user experience. Despite extensive optimization for mainstream queries, existing systems still struggle with long-tail queries, especially knowledge-intensive ones. These queries exhibit diverse linguistic patterns, often lack explicit purchase intent, and require domain-specific knowledge reasoning for accurate interpretation. They also suffer from a shortage of reliable behavioral logs, which makes such queries a persistent challenge for retrieval optimization. To address these issues, we propose an efficient data synthesis framework tailored to retrieval involving long-tail, knowledge-intensive queries. The key idea is to implicitly distill the capabilities of a powerful offline query-rewriting model into an efficient online retrieval system. Leveraging the strong language understanding of LLMs, we train a multi-candidate query rewriting model with multiple reward signals and capture its rewriting capability in well-curated query-product pairs through a powerful offline retrieval pipeline. This design mitigates distributional shift in rewritten queries, which might otherwise limit incremental recall or introduce irrelevant products. Experiments demonstrate that without any additional tricks, simply incorporating this synthetic data into retrieval model training leads to significant improvements. Online Side-By-Side (SBS) human evaluation results indicate a notable enhancement in user search experience.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper outlines a practical pipeline for turning LLM-based query rewriting into synthetic pairs to train retrieval models on long-tail e-commerce queries, but the abstract supplies no numbers or checks to show the gains are real or come from the claimed mechanism.

read the letter

The main point is that the authors describe an offline pipeline that uses a multi-reward LLM rewriting model, then runs those rewrites through a strong retrieval system to create curated query-product pairs for training an online retriever. The goal is to help with knowledge-intensive long-tail queries that lack behavioral data. They claim this mitigates distributional shift and leads to better recall without extra tricks, backed by positive side-by-side human results. That framing is straightforward and targets a real commercial bottleneck in e-commerce search. The approach of distilling rewriting capability into fixed pairs rather than running the LLM at inference time is a sensible engineering choice for scale. It also builds on existing ideas around data augmentation for retrieval but applies them specifically to the long-tail knowledge gap. What stands out is the emphasis on curation through the offline pipeline to avoid injecting irrelevant items. The soft spots are clear from the abstract alone. No metrics appear—no recall deltas, no baseline comparisons, no dataset sizes, no ablation on the reward signals or curation step. Without those, it is impossible to judge whether the synthetic data actually improves things or just adds noise, especially since the stress-test concern about query distribution and relevance is not addressed with any quantitative check like embedding distances or human relevance rates. The claim that gains come simply from adding the data rests on an unverified assumption that the pairs are high-quality and distributionally close. If the full paper includes detailed experiments, ablations, and those checks, the work becomes more credible for practitioners. As presented, the evidence is too thin to evaluate the central mechanism. This is aimed at applied IR teams building e-commerce retrieval systems who need ways to handle sparse queries. It is not foundational, but the problem is persistent enough that a solid version would be worth referee time. I would send it to peer review to see the full results and data.

Referee Report

2 major / 0 minor

Summary. The manuscript presents a data synthesis framework for e-commerce product retrieval targeting long-tail knowledge-intensive queries. It trains a multi-candidate query-rewriting model using LLMs and multiple reward signals, then curates synthetic query-product pairs via an offline retrieval pipeline to distill rewriting capabilities; these pairs are added to retrieval model training data, with the claim that this yields significant performance gains and improved user experience per online side-by-side (SBS) human evaluations.

Significance. If the empirical claims hold with detailed validation, the work offers a practical method to augment sparse behavioral data for difficult query types by leveraging offline LLM capabilities, potentially raising recall in large-scale retrieval systems without increasing online inference costs.

major comments (2)

[Abstract] Abstract: the central claim that 'simply incorporating this synthetic data into retrieval model training leads to significant improvements' is unsupported by any reported metrics, baselines, dataset sizes, ablation results, or statistical details, rendering it impossible to assess effect size or robustness.
[Abstract] Abstract: the assertion that the offline pipeline 'mitigates distributional shift' (which might otherwise limit incremental recall or introduce irrelevant products) lacks any quantitative check such as embedding distances between original and rewritten queries or human relevance rates on the curated pairs; this directly bears on whether the added data improves or degrades training.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the abstract requires more specific empirical details to support its claims and have revised it accordingly to include key metrics, baselines, and quantitative validations drawn from the full manuscript. We address each major comment below.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that 'simply incorporating this synthetic data into retrieval model training leads to significant improvements' is unsupported by any reported metrics, baselines, dataset sizes, ablation results, or statistical details, rendering it impossible to assess effect size or robustness.

Authors: We agree the abstract should be strengthened with concrete details. The full manuscript (Section 4) reports the relevant offline metrics (Recall@K gains on long-tail queries), baseline comparisons (including dense retrievers and BM25), synthetic dataset scale, ablation results on reward signals and pipeline components, and statistical significance. We have revised the abstract to summarize these elements with specific effect sizes. revision: yes
Referee: [Abstract] Abstract: the assertion that the offline pipeline 'mitigates distributional shift' (which might otherwise limit incremental recall or introduce irrelevant products) lacks any quantitative check such as embedding distances between original and rewritten queries or human relevance rates on the curated pairs; this directly bears on whether the added data improves or degrades training.

Authors: We acknowledge the value of explicit quantitative checks. The manuscript describes the offline retrieval pipeline's role in curating pairs to reduce shift, and we have added supporting analysis (embedding cosine similarities and human relevance rates on curated pairs) to the revised version. These will be summarized in the updated abstract. revision: yes

Circularity Check

0 steps flagged

No circularity: gains derive from external LLM rewriting and independent offline pipeline

full rationale

The paper presents a data-synthesis pipeline that uses an external LLM-based query-rewriting model plus a separate offline retrieval stage to curate synthetic query-product pairs; these pairs are then added to training data for the target retrieval model. No equations, fitted parameters, or self-citations are shown that would force the reported recall or SBS gains to equal the inputs by construction. The central claim therefore rests on the empirical assumption that the generated pairs improve coverage of long-tail queries—an assumption that is falsifiable against held-out traffic and not tautological.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides insufficient technical detail to enumerate free parameters, axioms, or invented entities; the approach appears to rest on standard LLM capabilities and existing retrieval pipelines without introducing new postulated objects.

pith-pipeline@v0.9.0 · 5556 in / 1050 out tokens · 20848 ms · 2026-05-15T19:25:17.849415+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages · 4 internal anchors

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Shahla Farzana, Qunzhi Zhou, and Petar Ristoski. 2023. Knowledge graph- enhanced neural query rewriting. InCompanion Proceedings of the ACM Web Conference 2023. 911–919

work page 2023

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Yunling Feng, Gui Ling, Yue Jiang, Jianfeng Huang, Dan Ou, Qingwen Liu, Fuyu Lv, and Yajing Xu. 2025. Complicated Semantic Alignment for Long-Tail Query Rewriting in Taobao Search Based on Large Language Model. InProceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2. 4435–4446

work page 2025

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Jian Hu, Jason Klein Liu, Haotian Xu, and Wei Shen. 2025. Reinforce++: An efficient rlhf algorithm with robustness to both prompt and reward models.arXiv preprint arXiv:2501.03262(2025)

work page internal anchor Pith review Pith/arXiv arXiv 2025

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Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. InProceedings of the 22nd ACM international conference on Information & Knowledge Management. 2333–2338

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Mingming Li, Huimu Wang, Zuxu Chen, Guangtao Nie, Yiming Qiu, Guoyu Tang, Lin Liu, and Jingwei Zhuo. 2024. Generative retrieval with preference optimization for e-commerce search.arXiv preprint arXiv:2407.19829(2024)

work page arXiv 2024

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Xingxian Liu, Dongshuai Li, Tao Wen, Jiahui Wan, Gui Ling, Fuyu Lv, Dan Ou, and Haihong Tang. 2025. Taosearchemb: A multi-objective reinforcement learning framework for dense retrieval in taobao search.arXiv preprint arXiv:2511.13885 (2025)

work page arXiv 2025

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Duy A Nguyen, Rishi Kesav Mohan, Van Yang, Pritom Saha Akash, and Kevin Chen-Chuan Chang. 2025. RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems.arXiv preprint arXiv:2501.18056(2025)

work page arXiv 2025

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Wenjun Peng, Guiyang Li, Yue Jiang, Zilong Wang, Dan Ou, Xiaoyi Zeng, Derong Xu, Tong Xu, and Enhong Chen. 2024. Large language model based long-tail query rewriting in taobao search. InCompanion Proceedings of the ACM Web Conference 2024. 20–28

work page 2024

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Yiming Qiu, Kang Zhang, Han Zhang, Songlin Wang, Sulong Xu, Yun Xiao, Bo Long, and Wen-Yun Yang. 2021. Query rewriting via cycle-consistent transla- tion for e-commerce search. In2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2435–2446

work page 2021

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Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks.arXiv preprint arXiv:1908.10084(2019)

work page internal anchor Pith review Pith/arXiv arXiv 2019

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Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A Smith, Luke Zettlemoyer, and Tao Yu. 2022. One embedder, any task: Instruction-finetuned text embeddings.arXiv preprint arXiv:2212.09741 (2022)

work page arXiv 2022

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Krysta M Svore and Christopher JC Burges. 2009. A machine learning approach for improved BM25 retrieval. InProceedings of the 18th ACM conference on Infor- mation and knowledge management. 1811–1814

work page 2009

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An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. 2025. Qwen3 technical report.arXiv preprint arXiv:2505.09388(2025)

work page internal anchor Pith review Pith/arXiv arXiv 2025

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Jianhui Yang, Yiming Jin, Pengkun Jiao, Chenhe Dong, Zerui Huang, Shaowei Yao, Xiaojiang Zhou, Dan Ou, and Haihong Tang. 2025. TaoSR-AGRL: Adaptive Guided Reinforcement Learning Framework for E-commerce Search Relevance. arXiv preprint arXiv:2510.08048(2025)

work page arXiv 2025

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Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, and Ed Chi. 2019. Sampling-bias-corrected neural modeling for large corpus item recommendations. InProceedings of the 13th ACM conference on recommender systems. 269–277

work page 2019

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Yanzhao Zhang, Mingxin Li, Dingkun Long, Xin Zhang, Huan Lin, Baosong Yang, Pengjun Xie, An Yang, Dayiheng Liu, Junyang Lin, et al. 2025. Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.arXiv preprint arXiv:2506.05176(2025)

work page internal anchor Pith review Pith/arXiv arXiv 2025