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

Fuente: arXiv

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Bibliographic Details
Main Authors:	Menon, Karthik, Haider, Batool Arhamna, Arham, Muhammad, Mehreen, Kanwal, Kadiyala, Ram Mohan Rao, Farooq, Hamza
Format:	Preprint
Published:	2025
Subjects:	Information Retrieval Artificial Intelligence Computation and Language Machine Learning
Online Access:	Acceder al recurso
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author	Menon, Karthik Haider, Batool Arhamna Arham, Muhammad Mehreen, Kanwal Kadiyala, Ram Mohan Rao Farooq, Hamza
author_facet	Menon, Karthik Haider, Batool Arhamna Arham, Muhammad Mehreen, Kanwal Kadiyala, Ram Mohan Rao Farooq, Hamza
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2508_04683
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Query Attribute Modeling: Improving search relevance with Semantic Search and Meta Data Filtering Menon, Karthik Haider, Batool Arhamna Arham, Muhammad Mehreen, Kanwal Kadiyala, Ram Mohan Rao Farooq, Hamza Information Retrieval Artificial Intelligence Computation and Language Machine Learning 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.
title	Query Attribute Modeling: Improving search relevance with Semantic Search and Meta Data Filtering
topic	Information Retrieval Artificial Intelligence Computation and Language Machine Learning
url	https://arxiv.org/abs/2508.04683