Graph-Based Data Models for Real-Time Recommendation Systems

Author(s)	Ankur Partap Kotwal
Country	United States
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.
Keywords	Graph Neural Networks, Recommendation Systems, Real-time Processing, Graph Convolutional Networks, Graph Attention Networks.
Field	Computer
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-31
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.34265
Short DOI	https://doi.org/g82ggk

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International Journal For Multidisciplinary Research