Improve User-Experience of E-Commerce Website by Graph Analysis with Deep Learning
Journal
Proceedings - 2019 IEEE 5th International Conference on Big Data Intelligence and Computing, DataCom 2019
Journal Volume
18
Start Page
230
End Page
233
ISBN (of the container)
9781728141176
ISBN
9781728141176
Date Issued
2019-11-18
Author(s)
DOI
10.1109/DataCom.2019.00042
Abstract
Customers regard good user-experience (UX) as the key factor in online shopping. Therefore, e-commerce (EC) websites will track users' behaviors for analyzing their relations to products. According to these analysis results, the websites' layout/workflow and recommendation are tuned to improve UX. However, conventional analysis tools usually focus on user-to-product relations. They are not good at analyzing "big and graphical"user behavior data. In this paper, we develop a graph analysis system with deep learning GNN (graph neural network). Our system uses the standard format xAPI to record user behavior data. We use graph analysis to discover their relations for improving UX. Experiments are on a real EC website with about 6,000 users in 3 months. Our system can reduce over 28% browsing actions of users to reach the page of desired products.
Event(s)
5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019, Kaohsiung, 18 November 2019 through 21 November 2019. Code 202644
Subjects
big data
deep learning
e-commerce
graph analysis
graph neural network
UX
xAPI
Publisher
IEEE
Type
conference paper