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Research Paper | Information Technology | India | Volume 13 Issue 3, March 2024 | Popularity: 5.3 / 10

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Leveraging Machine Learning for Personalization and Security in Content Management Systems

Venkata Sai Swaroop Reddy Nallapa Reddy

Abstract: Content Management Systems (CMS) play a pivotal role in creating, managing, and delivering digital content across various industries, including e - commerce, entertainment, education, and enterprise collaboration. With the increasing demands for hyper - personalized user experiences and robust security measures, the incorporation of machine learning (ML) into CMS workflows offers transformative potential. Traditional CMS platforms often struggle to adapt to the rapid evolution of user expectations and the sophistication of cyber threats. These challenges are particularly acute in environments handling large - scale, heterogeneous data, where static and reactive approaches fail to meet dynamic operational needs. This paper explores the integration of advanced ML techniques to enable dynamic content delivery, context - aware personalization, and robust threat detection. The proposed solutions leverage models for predictive analytics, natural language processing (NLP), anomaly detection, and adaptive security measures. Through comprehensive case studies and experimental validation, we demonstrate substantial improvements in user engagement, threat mitigation, scalability, and adaptability in modern CMS environments. Key findings indicate a 38% increase in click - through rates, a 94% success rate in threat detection, and significant reductions in operational latency and false positives. Additionally, this study addresses the implications of incorporating privacy - preserving techniques such as federated learning and distributed training methodologies. These approaches ensure data security while maintaining the performance and scalability of ML - driven systems. By presenting a future - proof architecture, this paper aims to guide CMS developers and researchers in implementing sustainable, ethical, and efficient solutions for next - generation content management.

Keywords: Artificial Intelligence (AI), Content Management Systems (CMS), Adobe Experience Manager (AEM), Machine Learning (ML), Workflow Automation, Personalized User Experience, Natural Language Processing (NLP), Computer Vision, Predictive Analytics, Digital Content Management

Edition: Volume 13 Issue 3, March 2024

Pages: 1943 - 1946

DOI: https://www.doi.org/10.21275/SR24037110821

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