Version 1
: Received: 25 July 2024 / Approved: 26 July 2024 / Online: 26 July 2024 (15:02:42 CEST)
How to cite:
Xu, K.; Zheng, H.; Zhan, X.; Zhou, S.; Niu, K. Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility. Preprints2024, 2024072199. https://doi.org/10.20944/preprints202407.2199.v1
Xu, K.; Zheng, H.; Zhan, X.; Zhou, S.; Niu, K. Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility. Preprints 2024, 2024072199. https://doi.org/10.20944/preprints202407.2199.v1
Xu, K.; Zheng, H.; Zhan, X.; Zhou, S.; Niu, K. Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility. Preprints2024, 2024072199. https://doi.org/10.20944/preprints202407.2199.v1
APA Style
Xu, K., Zheng, H., Zhan, X., Zhou, S., & Niu, K. (2024). Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility. Preprints. https://doi.org/10.20944/preprints202407.2199.v1
Chicago/Turabian Style
Xu, K., Shuwen Zhou and Kaiyi Niu. 2024 "Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility" Preprints. https://doi.org/10.20944/preprints202407.2199.v1
Abstract
This paper comprehensively explores the integration of cloud computing and advanced recommendation systems, emphasizing their pivotal roles in enhancing user experiences and operational efficiencies across digital platforms. It reviews the evolution of recommendation algorithms, highlighting their application in diverse domains such as e-commerce and media. The study evaluates the performance of advanced models like UniLLMRec against traditional counterparts using datasets from news and e-commerce domains. Additionally, the paper discusses the infrastructure architecture of cloud computing, demonstrating its capability to support scalable and efficient data processing. Through experimental insights and methodology, the research underscores the transformative impact of cloud technologies on optimizing recommendation system performance, thereby advancing digital engagement and competitiveness.
Keywords
Cloud Computing; Recommendation Systems; Artificial Intelligence; Big Data
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
