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Software Engineering Strategies for Real-Time Personalization in E-Commerce Recommendations
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Author(s): Kirti Jain (Jaypee Institute of Information Technology, India), Atishay Jain (Jaypee Institute of Information Technology, India), Aditya Bharadwaj (Jaypee Institute of Information Technology, India)and Ram Vashisth (Jaypee Institute of Information Technology, India)
Copyright: 2024
Pages: 14
Source title:
Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Amarjeet Prajapati (Jaypee Institute of Information Technology, India), Pancham Singh (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Mrignainy Kansal (Netaji Subhas University of Technology (NSUT), Delhi, India)
DOI: 10.4018/979-8-3693-3502-4.ch003
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Abstract
This chapter addresses the cold start problem, a significant challenge in e-commerce recommendation systems, through an innovative software engineering approach. Focused on personalized user engagement, the system employs a sophisticated collaborative filtering model strategically integrated within a robust software architecture. A key software engineering facet involves differentiating new and existing users using machine learning algorithms that scrutinize individual shopping behaviors. Leveraging collaborative filtering principles, the model intelligently analyzes similar users' purchasing patterns, ensuring a dynamic recommendation engine. The software engineering-driven integration supports accuracy and responsiveness, showcasing the transformative potential of adept software engineering strategies in revolutionizing personalized recommendations for e-commerce platforms.
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