Negative Data Mining for Contrastive Learning in Dense Retrieval at IKEA.com

• URL: http://arxiv.org/abs/2605.00353v1
• Date: Fri, 01 May 2026 02:32:02 GMT
• Title: Negative Data Mining for Contrastive Learning in Dense Retrieval at IKEA.com
• Abstract summary: We present a systematic approach to improving dense retrieval for IKEA product search through structured negative sampling strategies.<n>Our methodology achieves +2.6% average category accuracy on offline real user query experiments on the Canada market.<n>Our A/B test on long-tail queries showed no statistically significant differences in user engagement metrics between the improved and baseline models.
• Score: 0.5371337604556311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contrastive learning is a core component of modern retrieval systems, but its effectiveness heavily relies on the quality of negative examples used during training. In this work, we present a systematic approach to improving dense retrieval for IKEA product search through structured negative sampling strategies and scalable LLM-as-a-judge relevance evaluation. Building on IKEA Search Engine's late-interaction retrieval architectures, we introduce two key contributions: (1) structured negative sampling strategies that leverage product hierarchical taxonomy and product attributes to generate semantically challenging negatives, and (2) a comprehensive LLM-based evaluation methodology for generating training data. Rather than relying on sparse human annotations or random sampling, our LLM-based evaluation system allocates a score for all candidate products against each query. Our methodology achieves +2.6\% average category accuracy on offline real user query experiments on the Canada market. However, our A/B test on long-tail queries showed no statistically significant differences in user engagement metrics between the improved and baseline models ($p > 0.05$). We trace this gap to user search behavior: 67\% of popular searches exhibit zero-click rates above 50\%, indicating that a substantial proportion of search sessions result in no product engagement regardless of result ranking. These findings underscore the importance of hard negative mining but also the need for grounding training data and offline evals in real user search behavior -- including query intent distribution and zero-click patterns -- to bridge the gap between offline retrieval quality and online user engagement.

Related papers

• Rethinking the Evaluation and Optimization of LLM-Based Social Simulation [56.18577997572273]
  We use the subjectivity coefficient to analyze how accuracy-based evaluation and hard-label training fail as subjectivity grows.<n>We construct SUBJSIM, a benchmark of 19,300 contexts covering 193 annotators and 100 subjective questions.<n>Results on SUBJSIM demonstrate the advantages of our method.
  arXiv  Detail & Related papers  (2026-08-20T06:26:03Z)
• CoSearch: Joint Training of Reasoning and Document Ranking via Reinforcement Learning for Agentic Search [51.911048955965136]
  CoSearch is a framework that jointly trains a multi-step reasoning agent and a generative document ranking model.<n>Our results show that joint training of the reasoning agent and retrieval system is both feasible and strongly performant.
  arXiv  Detail & Related papers  (2026-04-19T17:48:17Z)
• Aligning Large Language Models with Searcher Preferences [26.974618053554394]
  We introduce SearchLLM, the first large language model (LLM) for open-ended generative search.<n>We design a hierarchical, multi-dimensional reward system that separates bottom-line constraints.<n> offline evaluations and online A/B tests show improved generation quality and user engagement.
  arXiv  Detail & Related papers  (2026-03-11T06:44:30Z)
• What Works for 'Lost-in-the-Middle' in LLMs? A Study on GM-Extract and Mitigations [1.2879523047871226]
  GM-Extract is a novel benchmark dataset meticulously designed to evaluate LLM performance on retrieval of control variables.<n>We conduct a systematic evaluation of 7-8B parameter models on two multi-document tasks (key-value extraction and question-answering)<n>While a distinct U-shaped curve was not consistently observed, our analysis reveals a clear pattern of performance across models.
  arXiv  Detail & Related papers  (2025-11-17T20:50:50Z)
• TaoSearchEmb: A Multi-Objective Reinforcement Learning Framework for Dense Retrieval in Taobao Search [11.893855231479717]
  Retrieval-GRPO is a reinforcement learning-based dense retrieval framework.<n>It has been deployed on China's largest e-commerce platform.
  arXiv  Detail & Related papers  (2025-11-17T20:16:52Z)
• Improving E-commerce Search with Category-Aligned Retrieval [0.0]
  Category-Aligned Retrieval System (CARS) improves search relevance by first predicting the product category from a user's query and then boosting products within that category.<n>We introduce a novel method for creating "Trainable Category Prototypes" from query embeddings.
  arXiv  Detail & Related papers  (2025-09-03T20:43:52Z)
• SCAN: Structured Capability Assessment and Navigation for LLMs [54.54085382131134]
  textbfSCAN (Structured Capability Assessment and Navigation) is a practical framework that enables detailed characterization of Large Language Models.<n>SCAN incorporates four key components:.<n>TaxBuilder, which extracts capability-indicating tags from queries to construct a hierarchical taxonomy;.<n>RealMix, a query synthesis and filtering mechanism that ensures sufficient evaluation data for each capability tag;.<n>A PC$2$-based (Pre-Comparison-derived Criteria) LLM-as-a-Judge approach achieves significantly higher accuracy compared to classic LLM-as-a-Judge method
  arXiv  Detail & Related papers  (2025-05-10T16:52:40Z)
• LLM-Driven Usefulness Judgment for Web Search Evaluation [12.10711284043516]
  Evaluation is fundamental in optimizing search experiences and supporting diverse user intents in Information Retrieval (IR)<n>Traditional search evaluation methods primarily rely on relevance labels, which assess how well retrieved documents match a user's query.<n>In this paper, we explore an alternative approach: LLM-generated usefulness labels, which incorporate both implicit and explicit user behavior signals to evaluate document usefulness.
  arXiv  Detail & Related papers  (2025-04-19T20:38:09Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• A structured regression approach for evaluating model performance across intersectional subgroups [53.91682617836498]
  Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups. We introduce a structured regression approach to disaggregated evaluation that we demonstrate can yield reliable system performance estimates even for very small subgroups.
  arXiv  Detail & Related papers  (2024-01-26T14:21:45Z)
• Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks [58.469818546042696]
  We study the sample efficiency of OPE with human preference and establish a statistical guarantee for it. By appropriately selecting the size of a ReLU network, we show that one can leverage any low-dimensional manifold structure in the Markov decision process.
  arXiv  Detail & Related papers  (2023-10-16T16:27:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.