Accepted Unknow ; Published 2024 November 2

Journal of Computer Technology and Applied Mathematics, 2024, 1(4), 3007-4126.

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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.

Personalized Search , Deep Learning , Cloud Computing , Scalable Architecture .

APA Style

Wang, J., Lu, T., Li, L. & Huang, D. (2024). Enhancing personalized search with ai: a hybrid approach integrating deep learning and cloud computing. Journal of Computer Technology and Applied Mathematics, 1(4), 89-99. https://doi.org/10.5281/zenodo.13998900

I would like to extend my sincere gratitude to Shiji Zhou, Bo Yuan, Kangming Xu, Mingxuan Zhang, and Wenxuan Zheng for their insightful research on cloud computing pricing schemes and their impact on distributed systems, as published in their article titled "The Impact of Pricing Schemes on Cloud Computing and Distributed Systems"[41]. Their comprehensive analysis of various pricing models and their effects on system performance has significantly influenced my understanding of cloud resource management and has provided valuable inspiration for the cloud infrastructure design in this study. I would also like to express my heartfelt appreciation to Fu Shang, Fanyi Zhao, Mingxuan Zhang, Jun Sun, and Jiatu Shi for their innovative work on integrating large language models with personalized recommendation systems, as detailed in their article "Personalized Recommendation Systems Powered by Large Language Models: Integrating Semantic Understanding and User Preferences"[42]. Their novel approach to combining semantic understanding with user preferences has greatly enhanced my knowledge of advanced personalization techniques and has inspired the deep learning component of our hybrid search system. The insights and methodologies presented in both these works have been instrumental in shaping the direction and implementation of the current research. Their contributions to the fields of cloud computing, distributed systems, and personalized recommendation have provided a solid foundation upon which this study has been built.

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