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

Jiayi Wang; Tianyu Lu; Lin Li; Decheng Huang

doi:10.5281/zenodo.13998900

Authors

Jiayi Wang Illinois institute of technology
Tianyu Lu Northeastern university
Lin Li Carnegie Mellon University
Decheng Huang University of Pennsylvania

DOI:

https://doi.org/10.5281/zenodo.13998900

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n4a11

Disciplines:

Computer Science

Subjects:

Deep Learning

References:

42

Keywords:

Personalized Search, Deep Learning, Cloud Computing, Scalable Architecture

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.

Author Biographies

Jiayi Wang, Illinois institute of technology

Computer engineering, Illinois institute of technology, IL, USA.

Tianyu Lu, Northeastern university

Northeastern university, USA.

Lin Li, Carnegie Mellon University

Electrical and Computer Engineering, Carnegie Mellon University, PA, USA.

Decheng Huang, University of Pennsylvania

Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA.

References

Jayaraman, S., Ramachandran, M., Patan, R., Daneshmand, M., & Gandomi, A. H. (2020). Fuzzy Deep Neural Learning Based on Goodman and Kruskal's Gamma for Search Engine Optimization. IEEE Transactions on Big Data, 8(1), 268-277.

Maabreh, M., Qolomany, B., Alsmadi, I., & Gupta, A. (2017, November). Deep learning-based MSMS spectra reduction in support of running multiple protein search engines on cloud. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1909-1914). IEEE.

Serrano, W., & Gelenbe, E. (2017, September). Intelligent search with deep learning clusters. In 2017 Intelligent Systems Conference (IntelliSys) (pp. 632-637). IEEE.

Srivastava, A., Nalluri, M., Lata, T., Ramadas, G., Sreekanth, N., & Vanjari, H. B. (2023, December). Scaling AI-Driven Solutions for Semantic Search. In 2023 International Conference on Power Energy, Environment & Intelligent Control (PEEIC) (pp. 1581-1586). IEEE.

Majumdar, S. (2022, September). The Changing Landscape of AI-Driven System Optimization for Complex Combinatorial Optimization. In Proceedings of the 2022 ACM/IEEE Workshop on Machine Learning for CAD (pp. 49-49).

Liu, Y., Tan, H., Cao, G., & Xu, Y. (2024). Enhancing User Engagement through Adaptive UI/UX Design: A Study on Personalized Mobile App Interfaces.

Huang, D., Yang, M., Wen, X., Xia, S., & Yuan, B. (2024). AI-Driven Drug Discovery: Accelerating the Development of Novel Therapeutics in Biopharmaceuticals. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 206-224.

Yang, M., Huang, D., Zhang, H., & Zheng, W. (2024). AI-Enabled Precision Medicine: Optimizing Treatment Strategies Through Genomic Data Analysis. Journal of Computer Technology and Applied Mathematics, 1(3), 73-84.

Wen, X., Shen, Q., Zheng, W., & Zhang, H. (2024). AI-Driven Solar Energy Generation and Smart Grid Integration A Holistic Approach to Enhancing Renewable Energy Efficiency. International Journal of Innovative Research in Engineering and Management, 11(4), 55-55.

Lou, Q. (2024). New Development of Administrative Prosecutorial Supervision with Chinese Characteristics in the New Era. Journal of Economic Theory and Business Management, 1(4), 79-88.

Liu, Y., Tan, H., Cao, G., & Xu, Y. (2024). Enhancing User Engagement through Adaptive UI/UX Design: A Study on Personalized Mobile App Interfaces.

Xu, H., Li, S., Niu, K., & Ping, G. (2024). Utilizing Deep Learning to Detect Fraud in Financial Transactions and Tax Reporting. Journal of Economic Theory and Business Management, 1(4), 61-71.

Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging Technologies in Finance: Revolutionizing Investment Strategies and Tax Management in the Digital Era. Management Journal for Advanced Research, 4(4), 35-49.

Shi J, Shang F, Zhou S, et al. Applications of Quantum Machine Learning in Large-Scale E-commerce Recommendation Systems: Enhancing Efficiency and Accuracy[J]. Journal of Industrial Engineering and Applied Science, 2024, 2(4): 90-103.

Wang, S., Zheng, H., Wen, X., & Fu, S. (2024). DISTRIBUTED HIGH-PERFORMANCE COMPUTING METHODS FOR ACCELERATING DEEP LEARNING TRAINING. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 108-126.

Lei, H., Wang, B., Shui, Z., Yang, P., & Liang, P. (2024). Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology. arXiv preprint arXiv:2404.04492.

Wang, B., Zheng, H., Qian, K., Zhan, X., & Wang, J. (2024). Edge computing and AI-driven intelligent traffic monitoring and optimization. Applied and Computational Engineering, 77, 225-230.

Xu, Y., Liu, Y., Xu, H., & Tan, H. (2024). AI-Driven UX/UI Design: Empirical Research and Applications in FinTech. International Journal of Innovative Research in Computer Science & Technology, 12(4), 99-109.

Li, H., Wang, S. X., Shang, F., Niu, K., & Song, R. (2024). Applications of Large Language Models in Cloud Computing: An Empirical Study Using Real-world Data. International Journal of Innovative Research in Computer Science & Technology, 12(4), 59-69.

Ping, G., Wang, S. X., Zhao, F., Wang, Z., & Zhang, X. (2024). Blockchain Based Reverse Logistics Data Tracking: An Innovative Approach to Enhance E-Waste Recycling Efficiency.

Xu, H., Niu, K., Lu, T., & Li, S. (2024). Leveraging artificial intelligence for enhanced risk management in financial services: Current applications and future prospects. Engineering Science & Technology Journal, 5(8), 2402-2426.

Shi, Y., Shang, F., Xu, Z., & Zhou, S. (2024). Emotion-Driven Deep Learning Recommendation Systems: Mining Preferences from User Reviews and Predicting Scores. Journal of Artificial Intelligence and Development, 3(1), 40-46.

Wang, Shikai, Kangming Xu, and Zhipeng Ling. "Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 77-87.

Ping, G., Zhu, M., Ling, Z., & Niu, K. (2024). Research on Optimizing Logistics Transportation Routes Using AI Large Models. Applied Science and Engineering Journal for Advanced Research, 3(4), 14-27.

Shang, F., Shi, J., Shi, Y., & Zhou, S. (2024). Enhancing E-Commerce Recommendation Systems with Deep Learning-based Sentiment Analysis of User Reviews. International Journal of Engineering and Management Research, 14(4), 19-34.

Xu, K., Zhou, H., Zheng, H., Zhu, M., & Xin, Q. (2024). Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning. arXiv preprint arXiv:2403.19345.

Xu, K., Zheng, H., Zhan, X., Zhou, S., & Niu, K. (2024). Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility.

Zheng, H., Xu, K., Zhou, H., Wang, Y., & Su, G. (2024). Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis. Academic Journal of Science and Technology, 10(1), 62-68.

Zheng, H.; Wu, J.; Song, R.; Guo, L.; Xu, Z. Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis. Applied and Computational Engineering 2024, 87, 26–32.

Zhan, X., Shi, C., Li, L., Xu, K., & Zheng, H. (2024). Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models. Applied and Computational Engineering, 71, 21-26.

Liang, P., Song, B., Zhan, X., Chen, Z., & Yuan, J. (2024). Automating the training and deployment of models in MLOps by integrating systems with machine learning. Applied and Computational Engineering, 67, 1-7.

Wu, B., Gong, Y., Zheng, H., Zhang, Y., Huang, J., & Xu, J. (2024). Enterprise cloud resource optimization and management based on cloud operations. Applied and Computational Engineering, 67, 8-14.

Liu, B., & Zhang, Y. (2023). Implementation of seamless assistance with Google Assistant leveraging cloud computing. Journal of Cloud Computing, 12(4), 1-15.

Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-based Code Completion. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 295-326.

Li, P., Hua, Y., Cao, Q., & Zhang, M. (2020, December). Improving the Restore Performance via Physical-Locality Middleware for Backup Systems. In Proceedings of the 21st International Middleware Conference (pp. 341-355).

Sun, J., Wen, X., Ping, G., & Zhang, M. (2024). Application of News Analysis Based on Large Language Models in Supply Chain Risk Prediction. Journal of Computer Technology and Applied Mathematics, 1(3), 55-65.

Zhao, F., Zhang, M., Zhou, S., & Lou, Q. (2024). Detection of Network Security Traffic Anomalies Based on Machine Learning KNN Method. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 1(1), 209-218.

Feng, Y., Qi, Y., Li, H., Wang, X., & Tian, J. (2024, July 11). Leveraging federated learning and edge computing for recommendation systems within cloud computing networks. In Proceedings of the Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024) (Vol. 13210, pp. 279-287). SPIE.

Zhao, F.; Li, H.; Niu, K.; Shi, J.; Song, R. Application of Deep Learning-Based Intrusion Detection System (IDS) in Network Anomaly Traffic Detection. Preprints 2024, 2024070595.

Gong, Y., Liu, H., Li, L., Tian, J., & Li, H. (2024, February 28). Deep learning-based medical image registration algorithm: Enhancing accuracy with dense connections and channel attention mechanisms. Journal of Theory and Practice of Engineering Science, 4(02), 1-7.

Zhou, S., Yuan, B., Xu, K., Zhang, M., & Zheng, W. (2024). The impact of pricing schemes on cloud computing and distributed systems. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(3), 193-205.

Shang, F., Zhao, F., Zhang, M., Sun, J., & Shi, J. (2024). Personalized recommendation systems powered by large language models: Integrating semantic understanding and user preferences. International Journal of Innovative Research in Engineering and Management, 11(4), 39-49.