Development of a Personalized Recommendation System for E-Commerce Products for Distributed Storage Systems

Area of Science:

• Computer Science
• Information Technology

Background:

• Distributed storage systems are increasingly popular due to their scalability and cost-effectiveness, supporting cloud, data center, and P2P services.
• E-commerce (b2c) platforms face challenges with inconsistent global development, requiring robust infrastructure for buying, selling, and logistics.
• High user traffic and the need for secure, efficient transactions are critical for e-commerce success.

Purpose of the Study:

• To explore the integration of distributed storage systems within e-commerce environments.
• To investigate the role of personalized recommendation systems in enhancing e-commerce user experience.
• To address the challenges of scalability and efficiency in online transaction platforms.

Main Methods:

• Analysis of distributed storage system architectures (e.g., GFS, HDFS).
• Examination of e-commerce platform requirements, including logistics and transaction processing.
• Development of rule-based personalized recommendation algorithms for user-specific item knowledge.

Main Results:

• Distributed storage systems provide a foundation for large-scale e-commerce data management.
• Personalized recommendation systems improve user engagement by offering tailored product suggestions.
• Efficient transaction processing and secure platforms are crucial for e-commerce growth.

Conclusions:

• Integrating advanced storage solutions and recommendation engines is key to optimizing e-commerce operations.
• Scalable and secure systems are essential for meeting the demands of a growing online marketplace.
• Personalization strategies significantly enhance the customer journey in b2c platforms.