Enhancing Personalized Search with AI: A Hybrid Approach Integrating Deep Learning and Cloud Computing

Abstract

This paper presents a novel hybrid approach for enhancing personalized search by integrating deep learning techniques with cloud computing infrastructure. The proposed system uses a multi-layer adaptive model augmented with a hierarchical monitoring network to capture user preferences and query semantics. Cloud-based architecture, used for Amazon Web Services, provides the necessary scalability and computing resources for the processing of large-scale research data. The system employs a custom middleware layer for efficient integration of the deep learning component with the distributed cloud infrastructure. An analysis of data on 100 million searches showed significant improvements in search accuracy and user satisfaction. The combined method achieves a 15% increase in Average Precision and a 12% improvement in Cost-effectiveness compared to the state-of-the-art baseline. Scalability analysis reveals the performance, maintaining sub-200ms latency for 95 percent. The system transforms the resource allocation efficiently into a non-volatile operation, demonstrating its potential for real-world deployment. This research contributes to the evolving field of AI-driven search optimization, solving problems in personal accuracy, scalability, and efficiency. The findings have implications for the design and implementation of ongoing research, providing insight into the integration of advanced machine learning with cloud resources.