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@article{173887,
author = {MOHAMED HAJITH M and Balakrishnan C and Aravind K and Sharath Kishan S},
title = {3D E-COMMERCE CUSTOMIZED WEBSITE POWERED BY AI},
journal = {International Journal of Innovative Research in Technology},
year = {2025},
volume = {11},
number = {10},
pages = {1863-1868},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=173887},
abstract = {The landscape of online retail has transformed dramatically, driven by consumer demand for immersive, personalized shopping experiences. This paper introduces a robust framework for a 3D e-commerce platform enhanced by Artificial Intelligence (AI), designed to exceed modern shopper expectations. Our framework integrates state-of-the-art technologies to create a seamless and interactive shopping environment. Central to its functionality is advanced 3D product visualization, offering users a tactile and comprehensive viewing experience akin to physical stores. AI-driven algorithms optimize rendering performance, ensuring visual fidelity across diverse devices and network conditions. Moreover, the platform incorporates sophisticated AI-powered recommendation systems. These systems analyze user preferences, browsing history, and contextual data to deliver highly personalized product suggestions in real-time. Continuous machine learning refinement ensures the relevance and accuracy of recommendations, thereby enhancing user engagement and driving conversion rates. Furthermore, our framework emphasizes the integration of AI-driven virtual assistants and chatbots equipped with natural language processing (NLP) capabilities. These virtual agents provide instant, intelligent customer support, handling inquiries ranging from product details to order tracking and post-purchase assistance. By offering proactive engagement and personalized interactions, these virtual assistants aim to elevate user satisfaction and foster enduring customer relationships.},
keywords = {3D product visualization, AI-driven algorithms, Natural Language Processing},
month = {March},
}
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