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

arXiv: https://arxiv.org/abs/2508.04683 · HTML: https://mlsanity.com/paper/2508.04683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)
- [Improving web search using contextual retrieval](https://mlsanity.com/paper/1407.6101/) (2014)
- [An Empirical Study of Uniform-Architecture Knowledge Distillation in Document Ranking](https://mlsanity.com/paper/2302.04112/) (2023)

## Built on by (most cited first, 1 of 1)

- [EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents](https://mlsanity.com/paper/2607.28229/) (2026)

## Abstract (arXiv)

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.