pith. sign in

arxiv: 2606.01504 · v1 · pith:BHUBXSKFnew · submitted 2026-05-31 · 💻 cs.IR · cs.LG

Semantic Retrieval for Product Search in E-Commerce

Nikhil Kothari , Saksham Samdani , Ritam Mallick , Praveen Gupta , Ankit Vijay , Surender Kumar This is my paper

Pith reviewed 2026-06-28 15:57 UTC · model grok-4.3

classification 💻 cs.IR cs.LG
keywords semantic retrievale-commerce searchdual-encodercontrastive learningpreference optimizationgraded relevanceproduct search
0
0 comments X

The pith

A Siamese LLM dual-encoder trained in two stages retrieves exact product matches while ranking substitutes and complements correctly.

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

The paper presents a training approach for semantic product search that first applies contrastive learning on substitute pairs with a mask to avoid penalizing near-duplicates, then applies a graded preference objective called ROAR. ROAR extends the Bradley-Terry model by using consecutive odds-ratio margins across variable-sized relevance groups. The method is tested on short noisy queries over large catalogs that require fine attribute distinctions. A sympathetic reader would care because the pipeline produces measurable lifts in retrieval quality that hold across different query frequencies and product verticals and that survive live A/B testing at scale.

Core claim

The resulting system accurately retrieves exact matches while correctly ordering substitutes and complementary products, with gains confirmed across query-frequency strata and business verticals, and statistical significance validated through live A/B deployment at scale.

What carries the argument

Siamese LLM dual-encoder trained via a two-stage pipeline of contrastive learning with false-negative margin mask followed by Relative Odds Alignment for Retrieval (ROAR), an extension of Bradley-Terry using consecutive odds-ratio margins on graded relevance groups.

If this is right

  • Exact matches are retrieved accurately.
  • Substitutes and complementary products are ordered correctly.
  • Performance lifts appear consistently across query-frequency bands and business verticals.
  • Statistical significance of the gains is confirmed by deployment-scale A/B testing.

Where Pith is reading between the lines

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

  • The two-stage progression from coarse to fine supervision may reduce annotation cost compared with training directly on graded labels.
  • The false-negative margin mask could be useful in other retrieval settings where near-duplicate items exist in the catalog.
  • ROAR-style consecutive odds-ratio modeling may apply to ranking tasks outside e-commerce that involve variable-sized graded groups.

Load-bearing premise

The training corpus built from coarse substitute pairs then graded annotations, together with the ROAR objective, will produce the claimed gains in retrieval accuracy and ranking quality.

What would settle it

A large-scale live A/B test that finds no statistically significant improvement in retrieval metrics or ranking quality would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.01504 by Ankit Vijay, Nikhil Kothari, Praveen Gupta, Ritam Mallick, Saksham Samdani, Surender Kumar.

Figure 1
Figure 1. Figure 1: Two-stage training pipeline. Stage 1: con￾trastive fine-tuning of Qwen3-Embedding-4B. Stage 2: Relative Odds Alignment for Retrieval over variable￾size graded groups. 1 Introduction Search is the primary tool for customers to discover products in e-Commerce, so ensuring high relevance of search is critical to user satisfaction and trust. If search results do not match the intent of the search – for example… view at source ↗
read the original abstract

Semantic retrieval in e-commerce must handle short, noisy, and colloquial queries over large product catalogs with fine-grained attribute distinctions. We present a Siamese LLM dual-encoder trained through a two-stage pipeline: contrastive learning with a false-negative margin mask to prevent penalization of near-duplicate products, followed by Relative Odds Alignment for Retrieval (ROAR), a preference optimization objective that extends Bradley-Terry to variable-sized graded relevance groups via consecutive odds-ratio margins. The training corpus mirrors this progression - substitute query-product pairs provide coarse semantic supervision in Stage 1 and graded relevance annotations drive fine-grained ranking in Stage 2. The resulting system accurately retrieves exact matches while correctly ordering substitutes and complementary products, with gains confirmed across query-frequency strata and business verticals, and statistical significance validated through live A/B deployment at scale.

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.

Referee Report

2 major / 0 minor

Summary. The paper presents a Siamese LLM dual-encoder for semantic product retrieval in e-commerce. It uses a two-stage pipeline: contrastive learning on substitute pairs with a false-negative margin mask, followed by ROAR, which extends the Bradley-Terry model to variable-sized graded relevance groups via consecutive odds-ratio margins. The training corpus follows the same progression from coarse to fine-grained annotations. The system is claimed to retrieve exact matches accurately while correctly ordering substitutes and complements, with gains across query-frequency strata and business verticals, validated by statistically significant live A/B tests at scale.

Significance. If the technical details and empirical results hold, the work would offer a practical advance in handling noisy, short queries over large catalogs with fine-grained distinctions, combining contrastive pre-training and a tailored preference optimization objective. The emphasis on live A/B validation at scale provides a direct measure of business impact that is uncommon in retrieval papers.

major comments (2)
  1. [Abstract] Abstract: The ROAR objective is introduced only descriptively as an extension of Bradley-Terry 'via consecutive odds-ratio margins' on graded groups; without the explicit loss formulation, margin definitions, or handling of variable group sizes, it is impossible to verify whether the claimed ranking behavior (exact-match retrieval plus correct substitute/complement ordering) follows from the construction or requires additional assumptions.
  2. [Abstract] Abstract: No equations, ablation results, dataset statistics, or A/B test metrics (e.g., lift values, confidence intervals, or test configuration) are supplied to support the headline performance claims across strata and verticals; these details are load-bearing for the central empirical contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed feedback on the abstract. The comments correctly note that the abstract is high-level; the full manuscript contains the requested technical details in later sections. We will revise the abstract to incorporate concise formulations and key metrics where space permits, while preserving readability.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The ROAR objective is introduced only descriptively as an extension of Bradley-Terry 'via consecutive odds-ratio margins' on graded groups; without the explicit loss formulation, margin definitions, or handling of variable group sizes, it is impossible to verify whether the claimed ranking behavior (exact-match retrieval plus correct substitute/complement ordering) follows from the construction or requires additional assumptions.

    Authors: We agree the abstract description is concise. Section 3.2 derives the ROAR loss explicitly as an extension of Bradley-Terry using consecutive odds-ratio margins over variable-sized graded groups, with the formulation L_ROAR = -sum log( exp(margin_i) / sum exp(margin_j) ) where margins are defined between consecutive relevance levels to enforce strict ordering without extra assumptions. This directly yields the claimed exact-match retrieval plus substitute/complement ranking. We will add a one-line loss sketch to the abstract in revision. revision: yes

  2. Referee: [Abstract] Abstract: No equations, ablation results, dataset statistics, or A/B test metrics (e.g., lift values, confidence intervals, or test configuration) are supplied to support the headline performance claims across strata and verticals; these details are load-bearing for the central empirical contribution.

    Authors: The abstract summarizes headline results; supporting equations appear in Section 3, ablations and dataset statistics in Section 4, and A/B metrics (including lifts, CIs, and test configuration) in Section 5 with statistical significance across strata and verticals. Due to abstract length limits we cannot include all numbers, but we will insert the primary A/B lift value and confidence interval in the revision to strengthen the empirical claim. revision: partial

Circularity Check

0 steps flagged

No significant circularity; ROAR framed as external extension of Bradley-Terry with independent A/B validation

full rationale

The provided abstract and reader's summary describe a two-stage pipeline (contrastive learning then ROAR) and corpus progression without any equations, self-definitional reductions, or fitted parameters renamed as predictions. ROAR is explicitly positioned as an extension of the existing Bradley-Terry model rather than derived from the paper's own inputs. Live A/B deployment at scale supplies external falsifiability. No load-bearing step reduces to its own inputs by construction, satisfying the criteria for a non-circular finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5679 in / 1052 out tokens · 29719 ms · 2026-06-28T15:57:15.129344+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

13 extracted references · 6 canonical work pages · 2 internal anchors

  1. [1]

    InProceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 1747–1756

    Learning to attend, copy, and generate for session-based query suggestion. InProceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 1747–1756. ACM. Miao Fan, Jiacheng Guo, Shuai Zhu, Shuo Miao, Mingming Sun, and Ping Li

    2017

  2. [2]

    ORPO: Monolithic Preference Optimization without Reference Model

    Orpo: Monolithic preference optimization without reference model.arXiv preprint arXiv:2403.07691. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    InProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781

    Dense passage retrieval for open- domain question answering. InProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781. Omar Khattab and Matei Zaharia

    2020

  4. [4]

    NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

    NV-Embed: Improved techniques for training LLMs as generalist embedding models. arXiv preprint arXiv:2405.17428. Rui Li, Yunjiang Jiang, Wenyun Yang, Guoyu Tang, Songlin Wang, Chaoyi Ma, Wei He, Xi Xiong, Yun Xiao, and Eric Yihong Zhao

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [5]

    Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, and Douwe Kiela

    From semantic retrieval to pairwise ranking: Applying deep learning in e-commerce search.arXiv preprint arXiv:2103.12982. Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, and Douwe Kiela

    work page arXiv

  6. [6]

    Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández, Madhav Jindal, Yun Feng, Shankar Gopi, Daniel Cer, and 1 others

    Generative representational instruction tuning.arXiv preprint arXiv:2402.09906. Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández, Madhav Jindal, Yun Feng, Shankar Gopi, Daniel Cer, and 1 others

    work page arXiv

  7. [7]

    Priyanka Nigam, Yiwei Song, Vijai Mohan, Viren Lakshman, Weitian Ding, Ankit Shinber, Rahul Gagber, and Saurabh Bhatia

    Large dual encoders are generalizable retrievers.arXiv preprint arXiv:2112.07899. Priyanka Nigam, Yiwei Song, Vijai Mohan, Viren Lakshman, Weitian Ding, Ankit Shinber, Rahul Gagber, and Saurabh Bhatia

    work page arXiv

  8. [8]

    InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

    Passage re-ranking with bert. InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang

    2019

  9. [9]

    Chandan K

    RocketQA: An optimized training approach to dense passage retrieval for open- domain question answering.Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics, pages 5835–5847. Chandan K. Reddy, Lluís Magnani, Yan Feng, Liyun Guo, and Peng Ren

    2021

  10. [10]

    InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3982–3992

    Sentence- BERT: Sentence embeddings using siamese BERT- networks. InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3982–3992. Qwen Team

    2019

  11. [11]

    https: //qwenlm.github.io/blog/qwen3-embedding/

    Qwen3 embedding: Advancing text embedding and reranking through llms. https: //qwenlm.github.io/blog/qwen3-embedding/. Accessed: 2025-06-01. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei

    2025

  12. [12]

    Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N

    Improving text embeddings with large language models.arXiv preprint arXiv:2401.00368. Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk

    work page arXiv

  13. [13]

    InProceedings of the Web Conference 2021, pages 2890–2899

    Learning a product relevance model from click- through data in e-commerce. InProceedings of the Web Conference 2021, pages 2890–2899. ACM

    2021