将用户搜索词拆解为结构化标签，提升电商搜索精准度
Query Attribute Modeling: Improving search relevance with Semantic Search and Meta Data Filtering
- 将自由文本查询分解为元数据标签和语义元素
- 在亚马逊玩具评论数据集上mAP@5达52.99%，显著优于传统方法
- 适合需要高精度检索的电商与企业搜索系统
本研究提出查询属性建模（QAM），一种混合框架，通过将开放式文本查询分解为结构化元数据标签和语义元素，提升搜索精度与相关性。QAM自动从自由文本查询中提取元数据过滤器，降低噪声，实现更聚焦的检索。基于包含10,000个独特商品、4万+条评论及详细产品属性的Amazon Toys Reviews数据集进行实验评估，QAM在mAP@5上达到52.99%。该性能显著优于传统方法，包括BM25关键词搜索、基于编码器的语义相似性搜索、交叉编码器重排序，以及结合BM25与语义结果的倒数排名融合（RRF）混合搜索。结果表明，QAM是企业搜索应用，尤其是电子商务系统中的可靠解决方案。
原文摘要 · Abstract (English)
This study introduces Query Attribute Modeling (QAM), a hybrid framework that enhances search precision and relevance by decomposing open text queries into structured metadata tags and semantic elements. QAM addresses traditional search limitations by automatically extracting metadata filters from free-form text queries, reducing noise and enabling focused retrieval of relevant items. Experimental evaluation using the Amazon Toys Reviews dataset (10,000 unique items with 40,000+ reviews and detailed product attributes) demonstrated QAM's superior performance, achieving a mean average precision at 5 (mAP@5) of 52.99\%. This represents significant improvement over conventional methods, including BM25 keyword search, encoder-based semantic similarity search, cross-encoder re-ranking, and hybrid search combining BM25 and semantic results via Reciprocal Rank Fusion (RRF). The results establish QAM as a robust solution for Enterprise Search applications, particularly in e-commerce systems.
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