Hybrid Semantic Search: Unveiling User Intent Beyond Keywords
This addresses the problem of improving search accuracy and intent understanding for users, though it appears incremental as it combines existing methods.
The paper tackles the limitations of keyword-based search in understanding user intent by introducing a hybrid approach that integrates keyword matching, semantic embeddings, and LLM-generated queries, resulting in highly relevant and contextually appropriate search outcomes.
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.