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

2025-08-06

AI Summary

Traditional open-ended textual queries struggle to precisely extract structured metadata and semantic elements, leading to suboptimal relevance in e-commerce search. To address this, we propose Query Attribute Modeling (QAM), a framework that automatically decomposes free-text queries into executable metadata filtering conditions and dense semantic representations. QAM integrates semantic search, BM25 retrieval, cross-encoder re-ranking, and reciprocal rank fusion (RRF) into a hybrid retrieval pipeline. By jointly optimizing structured constraints and semantic understanding, it overcomes the limitations of both pure keyword matching and end-to-end semantic models. Experiments on the Amazon Toys Reviews dataset demonstrate that QAM achieves 52.99% mAP@5—significantly outperforming BM25, standalone semantic search, and existing hybrid baselines. These results validate QAM’s effectiveness and practicality for real-world e-commerce search applications.

Technology Category

• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Intelligent Query Processing
• Natural Language Processing: Question Answering

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Enhances search precision by decomposing text queries
• Reduces noise in free-form query retrieval
• Improves e-commerce search relevance with metadata

Innovation

• Hybrid framework combining semantic and metadata search
• Automatic metadata extraction from free-form queries
• Improved search precision with structured query decomposition