# TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification

arXiv: https://arxiv.org/abs/2501.03835 · HTML: https://mlsanity.com/paper/2501.03835/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 48 references that are in the map.
- First posted on arXiv 7 Jan 2025.

## Builds on (most cited first, 24 of 24)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]](https://mlsanity.com/paper/1806.01264/) (2018)
- [MAVE: A Product Dataset for Multi-source Attribute Value Extraction](https://mlsanity.com/paper/2112.08663/) (2021)
- [Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product](https://mlsanity.com/paper/2009.07162/) (2020)
- [AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding](https://mlsanity.com/paper/2106.02318/) (2021)
- [TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories](https://mlsanity.com/paper/2004.13852/) (2020)
- [Product-Aware Answer Generation in E-Commerce Question-Answering](https://mlsanity.com/paper/1901.07696/) (2019)
- [OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision](https://mlsanity.com/paper/2204.13874/) (2022)
- [Using LLMs for the Extraction and Normalization of Product Attribute Values](https://mlsanity.com/paper/2403.02130/) (2024)
- [PAM: Understanding Product Images in Cross Product Category Attribute Extraction](https://mlsanity.com/paper/2106.04630/) (2021)
- [QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction](https://mlsanity.com/paper/2108.08468/) (2021)
- [Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction](https://mlsanity.com/paper/2206.14264/) (2022)
- [EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM](https://mlsanity.com/paper/2404.08886/) (2024)
- [Large Scale Generative Multimodal Attribute Extraction for E-commerce Attributes](https://mlsanity.com/paper/2306.00379/) (2023)
- [A Unified Generative Approach to Product Attribute-Value Identification](https://mlsanity.com/paper/2306.05605/) (2023)
- [An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification](https://mlsanity.com/paper/2407.01137/) (2024)
- [Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach](https://mlsanity.com/paper/2305.18350/) (2023)
- [IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing](https://mlsanity.com/paper/2410.16977/) (2024)
- [SAGE: Structured Attribute Value Generation for Billion-Scale Product Catalogs](https://mlsanity.com/paper/2309.05920/) (2023)
- [Exploring Large Language Models for Product Attribute Value Identification](https://mlsanity.com/paper/2409.12695/) (2024)

## Built on by (most cited first, 6 of 6)

- [GSID: Generative Semantic Indexing for E-Commerce Product Understanding](https://mlsanity.com/paper/2509.23860/) (2025)
- [MADIAVE: Multi-Agent Debate for Implicit Attribute Value Extraction](https://mlsanity.com/paper/2510.05611/) (2025)
- [LLP: LLM-Based Product Pricing in E-commerce](https://mlsanity.com/paper/2510.09347/) (2025)
- [Multi-Value-Product Retrieval-Augmented Generation for Industrial Product Attribute Value Identification](https://mlsanity.com/paper/2509.23874/) (2025)
- [JD Oxygen AI Item Center (Oxygen AIIC) V1: An Industrial-Scale LLM/VLM-Centric Solution for Item Understanding, Management, and Applications](https://mlsanity.com/paper/2606.28070/) (2026)
- [CVE-SAI: Counterfactual Visual Evidence-Guided Selective Attribute Indexing for Risk-Controlled E-commerce Search](https://mlsanity.com/paper/2608.25023/) (2026)

## Abstract (arXiv)

Product Attribute Value Identification (PAVI) involves identifying attribute values from product profiles, a key task for improving product search, recommendation, and business analytics on e-commerce platforms. However, existing PAVI methods face critical challenges, such as inferring implicit values, handling out-of-distribution (OOD) values, and producing normalized outputs. To address these limitations, we introduce Taxonomy-Aware Contrastive Learning Retrieval (TACLR), the first retrieval-based method for PAVI. TACLR formulates PAVI as an information retrieval task by encoding product profiles and candidate values into embeddings and retrieving values based on their similarity. It leverages contrastive training with taxonomy-aware hard negative sampling and employs adaptive inference with dynamic thresholds. TACLR 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. Extensive experiments on proprietary and public datasets validate the effectiveness and efficiency of TACLR. Further, it has been successfully deployed on the real-world e-commerce platform Xianyu, processing millions of product listings daily with frequently updated, large-scale attribute taxonomies. We release the code to facilitate reproducibility and future research at https://github.com/SuYindu/TACLR.