Developing an AI-Driven Personalization Engine for Real-Time Content Marketing in E-commerce Platforms

In the rapidly evolving landscape of e-commerce, the need for advanced personalization strategies has become paramount. This research endeavors to develop a cutting-edge Convolutional Neural Network (CNN)-driven personalization engine explicitly tailored for real-time content marketing in ecommerce platforms. The proposed system goes beyond traditional methods by dynamically analyzing user behavior in real-time, utilizing CNN’s capabilities to interpret and respond to visual elements within the e-commerce platform. The methodology involves the integration of CNN algorithms into the personalization engine architecture, coupled with extensive data collection from various sources, including user interactions, preferences, and historical data. The CNN-driven engine aims to provide instantaneous and personalized content recommendations, enhancing user experience and engagement. Evaluation metrics encompass user interaction patterns, clickthrough rates, and conversion rates. This research contributes a novel perspective to the field of e-commerce personalization by leveraging CNN technology, promising not only enhanced accuracy in content recommendations but also the ability to adapt in real-time to the ever-changing preferences of users. With an accuracy rate of $\mathbf{9 8 \%}$, the engine demonstrates a high level of correctness in its recommendations, capturing both relevant and non-relevant items with precision. The findings hold significant implications for the future of real-time content marketing in the competitive e-commerce landscape.