TACLR: A Scalable and Efficient Retrieval-based Method for Industrial Product Attribute Value Identification (2501.03835v4)

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 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.

• The paper presents TACLR, a retrieval-based framework employing taxonomy-aware contrastive learning to robustly identify product attribute values, achieving high F1 scores.
• The method reframes product attribute identification as an information retrieval task that scales to thousands of categories and processes millions of items daily.
• Practical evaluations show TACLR’s strong generalization and balanced precision-recall performance, making it ideal for high-throughput industrial applications.

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

The paper introduces Taxonomy-Aware Contrastive Learning Retrieval (TACLR), a novel method for Product Attribute Value Identification (PAVI) on e-commerce platforms. This task involves identifying product attribute values from supplier data to enhance product search, recommendations, and analytics. The authors address key challenges faced by existing PAVI methods, such as inferring implicit attribute values, handling out-of-distribution (OOD) values, and ensuring output normalization.

Methodology

TACLR formulates PAVI as an information retrieval problem. It encodes product entries and candidate attribute values into embeddings and determines matches through similarity measures. The approach leverages contrastive learning with taxonomy-aware negative sampling, which selects difficult negatives from the same attribute category to refine model performance. TACLR supports scalability to thousands of categories and attributes and efficiently processes millions of items daily in industrial applications.

Key features include:

1. Handling Implicit and OOD Values: TACLR can infer non-explicit values and generalize beyond the training dataset, a significant improvement over classification-based methods.
2. Contrastive Learning: Inspired by CLIP, the use of taxonomy-aware contrastive learning enhances the discrimination ability of value embeddings, employing adaptive inference with dynamic thresholds derived from relevance scores of null values.
3. Scalability and Efficiency: Unlike generative approaches that are computationally intensive, TACLR maintains high processing throughput suitable for e-commerce scale environments.

Experimental Results

The authors validate TACLR’s effectiveness via experiments on proprietary and public datasets, including Ecom-PAVI and WDC-PAVE. TACLR achieved high F1 scores, outperforming both generation-based and classification-based baselines across distinct product datasets. For instance, TACLR showed a significant F1 score of 86.2% on the Ecom-PAVI dataset and demonstrated strong generalization abilities. Moreover, TACLR effectively balanced precision and recall by dynamically adjusting inference thresholds.

Theoretical Implications

The deployment of TACLR highlights a shift towards more scalable retrieval-based frameworks for industrial applications in AI. By using a structural understanding of the attribute taxonomy, TACLR efficiently models complex e-commerce needs while adapting to real-time requirements. The use of adaptive dynamic thresholds also signals a broader trend towards contextually aware retrieval systems in AI, advancing traditional retrieval-based techniques.

Practical Implications

Practically, TACLR’s application within an e-commerce platform suggests it is well-suited for operational environments with high data throughput and dynamic taxonomies. The method's adaptability and robust performance across diverse datasets underscore its potential for broader deployment across various domains that require structured data retrieval, such as inventory management and real-time product analysis.

Future Developments

Future work could explore integrating TACLR with multimodal information, such as image or video data, to capture additional implicit product attributes. Additionally, the framework's adaptability to other e-commerce contexts or non-commercial applications may yield further benefits, particularly in domains requiring large-scale attribute value identification.

In summary, TACLR stands as a comprehensive approach that addresses existing limitations in PAVI methods, blending efficiency with scalability and positioning itself as a robust solution for large-scale industrial applications in AI-powered e-commerce platforms.