Query Attribute Modeling: Improving search relevance with Semantic Search and Meta Data Filtering

TL;DR

This paper presents Query Attribute Modeling (QAM), a hybrid approach that improves search relevance by extracting structured metadata and semantic features from free text queries, outperforming traditional methods in e-commerce search tasks.

Contribution

Introduction of QAM, a novel hybrid framework that automatically decomposes free text queries into structured metadata and semantic components for enhanced search precision.

Findings

QAM achieved a mean average precision at 5 (mAP@5) of 52.99%.

QAM outperformed BM25, semantic similarity, cross-encoder re-ranking, and hybrid RRF methods.

QAM is effective for enterprise e-commerce search applications.

Abstract

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.