Year: 2024
Type: article
Abstract: Graph-based data models have emerged as a transformative approach in real-time recommendation systems across diverse domains. While traditional recommendation methods have served their purpose, the growing complexity of user-item relationships necessitates more sophisticated solutions. This article presents an in-depth analysis of graph-based data models for building real-time recommendation systems, examining their enhanced capabilities in scalability and relationship modeling. The article explores various graph neural network architectures, implementation considerations, and real-world applications, demonstrating significant improvements in recommendation quality, processing efficiency, and system scalability compared to conventional approaches.
Author: Ankur Partap Kotwal -
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4406632511