The proposed system processes user queries using semantics analysis and generates contextual embeddings to better understand the meaning behind the search terms. It then applies similarity scoring and intelligent ranking algorithms to retrieve and order the most relevant results. The system architecture consists of modules for query preprocessing, semantics representation, document indexing ,similarity computation and dynamics ranking. The ranking components design to adapt over time by incorporating user interaction data, enabling continuous learning and personalized search experiences. Experimental evolution shows that the proposed AI-based approach outperform traditional keyword based system in terms of precision ,recall, and Mean Reciprocal Rank(MRR).The system demonstrates improved contextual understanding ,faster response times, and higher user satisfaction .The proposed solution is suitable for applications such as e-commerce platform, academics search engines, enterprise knowledge management systems ,and digital libraries. The results indicate that integrating artificial intelligence into search and ranking mechanisms significantly enhances the effectiveness and efficiency of modern information retrieval system.
Artificial Intelligence , Information Retrieval, Search Engine ,Natural Language Processing, Machine Learning, Ranking Algorithms
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