Hierarchical Multi-field Representations for Two-Stage E-commerce Retrieval

ACL ARR 2025 May Submission3796 Authors

Abstract: Dense retrieval methods typically target unstructured text data represented as flat strings. However, e-commerce catalogs often include structured information across multiple fields, such as brand, title, and description, which contain important information potential for retrieval systems. We present the the Cascading Hierarchical Attention Retrieval Model (CHARM), a novel framework designed to encode structured product data into hierarchical field-level representations with progressively finer detail. Utilizing a novel block-triangular attention mechanism, our method captures the inter-dependencies between product fields in a specified hierarchy, yielding field-level representations and aggregated vectors suitable for fast and efficient retrieval. Combining both representations enables a two-stage retrieval pipeline, in which the aggregated vectors support initial candidate selection, while more expressive field-level representations facilitate precise fine-tuning for downstream ranking. Experiments on publicly available large-scale e-commerce datasets demonstrate that CHARM outperforms state-of-the-art baselines. Our analysis highlights the framework’s ability to align different queries with appropriate product fields, enhancing retrieval accuracy and explainability.

Paper Type: Long

Research Area: Information Retrieval and Text Mining

Research Area Keywords: dense retrieval, document representation, re-ranking, contrastive learning

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English, Spanish, Japanese

Submission Number: 3796