Artificial neural network based high dimensional data visualization technique for interactive data exploration in E-commerce

In recent days, data-driven corporate landscape data visualization has been essential and regularly used to help make decisions closely connected to many e-commerce firms' main profits. Since there is an increased need for database professionals to support reliable and successful data visualization, the high demand for data processing is based on data volume, speed, and data integrity. Currently, large-data visualization applications are complicated and varied. There are no standardized and uniform application creation methods, resulting in low visualization applications becoming reusable and challenging to extend and sustain. This paper suggests an Artificial Neural Network (ANN) based High Dimensional Data Visualization Technique for Interactive Data Exploration ((HDVT)-V-2-IDE) in e-commerce. In this study, the ANN applies to the e-commerce product advertising recommendation for predicting the advertising click rate. The proposed framework has been evaluated with the existing models and observed the highest performance in terms of prediction accuracy (62.4%) and customer consistency rate (92.1%).

Automated summary

Data-driven corporate landscape data visualization plays a crucial role in e-commerce. However, the current large-data visualization applications are complex and lack standardized creation methods, making them difficult to reuse and extend. This study proposes an artificial neural network-based high dimensional data visualization technique and applies it to e-commerce product advertising recommendation, achieving high prediction accuracy and customer consistency rates.