Product Attribute Value Identification (PAVI) involves identifying attribute values from product profiles, a key task for improving product search, recommendations, 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 to the item embedding. 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 scenarios. Extensive experiments on proprietary and public datasets validate the effectiveness and efficiency of TACLR. Moreover, it has been successfully deployed in a real-world e-commerce platform, processing millions of product listings daily while supporting dynamic, large-scale attribute taxonomies.

 本研究解决了现有产品属性值识别（PAVI）方法在识别隐含值、处理分布外值以及生成标准化输出方面的关键挑战。我们提出了TACLR，它将PAVI视为信息检索任务，通过编码产品资料和候选值并基于相似性检索值，从而有效管理隐含和OOD值，支持大规模的属性分类。实验表明，TACLR在提高识别效率和准确性方面具有显著优势，并成功应用于实际电商平台。 

TACLR：一种可扩展高效的基于检索的工业产品属性值识别方法