用分层注意力建模商品多字段信息，提升电商检索精度。
Hierarchical Multi-field Representations for Two-Stage E-commerce Retrieval
- 设计分层注意力机制，逐级编码品牌、标题等字段信息。
- 在多个公开数据集上超越现有方法，显著提升检索效果。
- 适合需要精准商品匹配与可解释性的电商系统应用。
密集检索方法通常针对以扁平字符串形式表示的非结构化文本。然而，电商目录常包含品牌、标题、描述等多个字段的结构化信息，这些信息对检索系统至关重要。本文提出级联分层注意力检索模型（CHARM），一种将结构化商品数据编码为逐级细化的字段级表示的新框架。通过新颖的块三角注意力机制，该方法捕捉预设层级中各字段间的依赖关系，生成字段级表示和聚合向量，适用于快速高效的检索。结合两种表示构建两阶段检索流程：聚合向量用于初始候选筛选，字段级表示则支持下游排序的精细优化。在多个公开的大规模电商数据集上的实验表明，CHARM 达到或超越现有最佳基准。分析显示，该框架能有效将不同查询与相应商品字段对齐，提升检索准确率与可解释性。
原文摘要 · Abstract (English)
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 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 interdependencies 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 matches or 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.
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