Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2025
Abstract
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 outof-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, largescale attribute taxonomies. We release the code to facilitate reproducibility and future research at https://github.com/SuYindu/TACLR.
Discipline
Artificial Intelligence and Robotics
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), Vienna, Austria, July 27 - August 1
First Page
31526
Last Page
31538
Publisher
ACL
City or Country
Vienna, Austria
Citation
SU, Yindu; ZOU, Huike; SUN, Lin; ZHANG, Ting; YANG, Haiyang; YU, Chen Li; LO, David; ZHANG, Qingheng; HAN, Shuguang; and CHEN, Jufeng.
TACLR: a scalable and efficient retrieval-based method for industrial product attribute value identification. (2025). Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), Vienna, Austria, July 27 - August 1. 31526-31538.
Available at: https://ink.library.smu.edu.sg/sis_research/10832
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