Hybrid Semantic Search: Unveiling User Intent Beyond Keywords
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866912017177116672 |
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| author | Ahluwalia, Aman Sutradhar, Bishwajit Ghosh, Karishma Yadav, Indrapal Sheetal, Arpan Patil, Prashant |
| author_facet | Ahluwalia, Aman Sutradhar, Bishwajit Ghosh, Karishma Yadav, Indrapal Sheetal, Arpan Patil, Prashant |
| contents | This paper addresses the limitations of traditional keyword-based search in understanding user intent and introduces a novel hybrid search approach that leverages the strengths of non-semantic search engines, Large Language Models (LLMs), and embedding models. The proposed system integrates keyword matching, semantic vector embeddings, and LLM-generated structured queries to deliver highly relevant and contextually appropriate search results. By combining these complementary methods, the hybrid approach effectively captures both explicit and implicit user intent.The paper further explores techniques to optimize query execution for faster response times and demonstrates the effectiveness of this hybrid search model in producing comprehensive and accurate search outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09236 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hybrid Semantic Search: Unveiling User Intent Beyond Keywords Ahluwalia, Aman Sutradhar, Bishwajit Ghosh, Karishma Yadav, Indrapal Sheetal, Arpan Patil, Prashant Information Retrieval Artificial Intelligence This paper addresses the limitations of traditional keyword-based search in understanding user intent and introduces a novel hybrid search approach that leverages the strengths of non-semantic search engines, Large Language Models (LLMs), and embedding models. The proposed system integrates keyword matching, semantic vector embeddings, and LLM-generated structured queries to deliver highly relevant and contextually appropriate search results. By combining these complementary methods, the hybrid approach effectively captures both explicit and implicit user intent.The paper further explores techniques to optimize query execution for faster response times and demonstrates the effectiveness of this hybrid search model in producing comprehensive and accurate search outcomes. |
| title | Hybrid Semantic Search: Unveiling User Intent Beyond Keywords |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2408.09236 |