Open Access
American Research Journal of Computer Science and Information Technology
ISSN (Online): 2572-2921
DOI: 10.46568/arjcsit
Building a Graph Neural Network Model for E-Commerce
Abstract
The article discusses the development of graph neural network (GNN) models for e-commerce, aimed at predicting user
interactions with the content of the main page. The methods described focus on utilizing networks to improve relevance
and personalize the user experience.
The purpose of this work is to examine the features of the graph neural network architecture specifically designed for
e-commerce tasks. This architecture operates on graph data structures, allowing for the consideration of different levels
of connections between users and products, and their various features.
The methodology employs graph node embedding algorithms such as GraphSAGE and Node2Vec, which transform any
data into numerical vector representations. The sources used include scientific articles published by the author in the
public domain, as well as materials available on the Internet, enabling a comprehensive exploration of the topic.
The results demonstrate that the proposed architecture enhances the accuracy of content personalization, as evidenced by
increased click-through rates (CTR) and revenue. The implementation of this system modifies the algorithms for ranking
content, thereby impacting the platform’s effectiveness.
The article will be valuable for specialists working on recommendation systems, researchers, and developers of
e-commerce solutions. The conclusions affirm the success of the proposed architecture in data analysis and user experience
adaptation.
