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ONLINE SHOPPING: A CONTEXT-AWARE, ADVANCED MACHINE LEARNING ALGORITHMS AND ARCHITECTURE.

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Bibliographische Detailangaben
Titel: ONLINE SHOPPING: A CONTEXT-AWARE, ADVANCED MACHINE LEARNING ALGORITHMS AND ARCHITECTURE.
Autoren: YADAV, KHUSHI, JAISWAL, ARVIND
Quelle: i-Manager's Journal on Information Technology; Dec2025, Vol. 14 Issue 4, p44-51, 8p
Schlagwörter: ONLINE shopping, MACHINE learning, ELECTRONIC commerce, RECOMMENDER systems, SOFTWARE architecture, PAYMENT systems
Abstract: Online shopping websites play a vital role in modern digital commerce by enabling users to browse, compare, and purchase products conveniently. With the rapid growth of product catalogs, user data, and transactional information, traditional rule-based systems struggle to provide personalized experiences, efficient search, and intelligent recommendations. Modern e-commerce platforms leverage advanced technologies such as artificial intelligence, data analytics, recommendation systems, and secure backend architectures to enhance user engagement, search accuracy, and overall shopping experience. This paper presents a comprehensive analysis of an online shopping website, focusing on system architecture, core functionalities, and performance optimization techniques. The study includes product management, user authentication, shopping cart functionality, order processing, and secure payment integration. AI-driven features such as personalized product recommendations, smart search, sentiment analysis of reviews, and automated notifications significantly improve customer satisfaction and business efficiency. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:Online shopping websites play a vital role in modern digital commerce by enabling users to browse, compare, and purchase products conveniently. With the rapid growth of product catalogs, user data, and transactional information, traditional rule-based systems struggle to provide personalized experiences, efficient search, and intelligent recommendations. Modern e-commerce platforms leverage advanced technologies such as artificial intelligence, data analytics, recommendation systems, and secure backend architectures to enhance user engagement, search accuracy, and overall shopping experience. This paper presents a comprehensive analysis of an online shopping website, focusing on system architecture, core functionalities, and performance optimization techniques. The study includes product management, user authentication, shopping cart functionality, order processing, and secure payment integration. AI-driven features such as personalized product recommendations, smart search, sentiment analysis of reviews, and automated notifications significantly improve customer satisfaction and business efficiency. [ABSTRACT FROM AUTHOR]
ISSN:22775110
DOI:10.26634/jit.14.4.22872