Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification

• URL: http://arxiv.org/abs/2509.23874v1
• Date: Sun, 28 Sep 2025 13:29:20 GMT
• Title: Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification
• Authors: Huike Zou, Haiyang Yang, Yindu Su, Liyu Chen, Chengbao Lian, Qingheng Zhang, Shuguang Han, Jufeng Chen,
• Abstract summary: We introduce Multi-Value-Product Retrieval-Augmented Generation (MVP-RAG)<n>MVP-RAG defines PAVI as a retrieval-generation task, where the product title description serves as the query.<n>It first retrieves similar products of the same category and candidate attribute values, and then generates the standardized attribute values.
• Score: 12.167857724257239
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying attribute values from product profiles is a key task for improving product search, recommendation, and business analytics on e-commerce platforms, which we called Product Attribute Value Identification (PAVI) . However, existing PAVI methods face critical challenges, such as cascading errors, inability to handle out-of-distribution (OOD) attribute values, and lack of generalization capability. To address these limitations, we introduce Multi-Value-Product Retrieval-Augmented Generation (MVP-RAG), combining the strengths of retrieval, generation, and classification paradigms. MVP-RAG defines PAVI as a retrieval-generation task, where the product title description serves as the query, and products and attribute values act as the corpus. It first retrieves similar products of the same category and candidate attribute values, and then generates the standardized attribute values. The key advantages of this work are: (1) the proposal of a multi-level retrieval scheme, with products and attribute values as distinct hierarchical levels in PAVI domain (2) attribute value generation of large language model to significantly alleviate the OOD problem and (3) its successful deployment in a real-world industrial environment. Extensive experimental results demonstrate that MVP-RAG performs better than the state-of-the-art baselines.

Related papers

• TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification [19.911923049421137]
  We introduce Contrastive Learning Retrieval (TACLR), the first retrieval-based method for Product Attribute Value Identification (PAVI)<n>TACLR formulates PAVI as an information retrieval task by encoding product profiles and candidate values into embeddings and retrieving values based on their similarity.<n>It offers three key advantages: (1) it effectively handles implicit and OOD values while producing normalized outputs; (2) it scales to thousands of categories, tens of thousands of attributes, and millions of values; and (3) it supports efficient inference for high-load industrial deployment.
  arXiv  Detail & Related papers  (2025-01-07T14:45:30Z)
• Self-Refinement Strategies for LLM-based Product Attribute Value Extraction [51.45146101802871]
  This paper investigates applying two self-refinement techniques to the product attribute value extraction task.<n>The experiments show that both self-refinement techniques fail to significantly improve the extraction performance while substantially increasing processing costs.<n>For scenarios with development data, fine-tuning yields the highest performance, while the ramp-up costs of fine-tuning are balanced out as the amount of product descriptions increases.
  arXiv  Detail & Related papers  (2025-01-02T12:55:27Z)
• An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification [25.890927969633196]
  This paper provides the most comprehensive evaluation of Product Attribute and Value Identification (PAVI) so far. We compare three different attribute-value generation strategies based on fine-tuning encoder-decoder models on three datasets. Experiments show that end-to-end AVG approach, which is computationally efficient, outperforms other strategies.
  arXiv  Detail & Related papers  (2024-07-01T10:02:17Z)
• Using LLMs for the Extraction and Normalization of Product Attribute Values [47.098255866050835]
  This paper explores the potential of using large language models (LLMs) to extract and normalize attribute values from product titles and descriptions. We introduce the Web Data Commons - Product Attribute Value Extraction (WDC-PAVE) benchmark dataset for our experiments.
  arXiv  Detail & Related papers  (2024-03-04T15:39:59Z)
• ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction [52.14681890859275]
  E-commerce platforms require structured product data in the form of attribute-value pairs. BERT-based extraction methods require large amounts of task-specific training data. This paper explores using large language models (LLMs) as a more training-data efficient and robust alternative.
  arXiv  Detail & Related papers  (2023-10-19T07:39:00Z)
• SAGE: Structured Attribute Value Generation for Billion-Scale Product Catalogs [1.1184789007828977]
  SAGE is a Generative LLM for inferring attribute values for products across world-wide e-Commerce catalogs. We introduce a novel formulation of the attribute-value prediction problem as a Seq2Seq summarization task. SAGE is the first method able to tackle all aspects of the attribute-value-prediction task as they arise in practical settings in e-Commerce catalogs.
  arXiv  Detail & Related papers  (2023-09-12T02:24:16Z)
• A Unified Generative Approach to Product Attribute-Value Identification [6.752749933406399]
  We explore a generative approach to the product attribute-value identification (PAVI) task. We finetune a pre-trained generative model, T5, to decode a set of attribute-value pairs as a target sequence from the given product text. Experimental results confirm that our generation-based approach outperforms the existing extraction and classification-based methods.
  arXiv  Detail & Related papers  (2023-06-09T00:33:30Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision [93.26737878221073]
  We study the attribute mining problem in an open-world setting to extract novel attributes and their values. We propose a principled framework that first generates attribute value candidates and then groups them into clusters of attributes. Our model significantly outperforms strong baselines and can generalize to unseen attributes and product types.
  arXiv  Detail & Related papers  (2022-04-29T04:16:04Z)
• Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product [40.46223408546036]
  Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product recommendations, and product retrieval. While in the real world, the attribute values of a product are usually incomplete and vary over time, which greatly hinders the practical applications. We propose a multimodal method to jointly predict product attributes and extract values from textual product descriptions with the help of the product images.
  arXiv  Detail & Related papers  (2020-09-15T15:10:51Z)
• Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data [61.789797281676606]
  We propose a novel meta-learning latent variable approach, called MetaBridge. It can learn transferable knowledge from a subset of categories with limited labeled data. It can capture the uncertainty of never-seen categories with unlabeled data.
  arXiv  Detail & Related papers  (2020-06-15T21:31:05Z)

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.