Friendship is All we Need: A Multi-graph Embedding Approach for Modeling Customer Behavior

Abstract

Understanding customer behavior is fundamental for many use-cases in industry, especially in accelerated growth areas such as fin-tech and e-commerce. Structured data are often expensive, time-consuming and inadequate to analyze and study complex customer behaviors. In this paper, we propose a multi-graph embedding approach for creating a non-linear representation of customers in order to have a better knowledge of their characteristics without having any prior information about their financial status or their interests. By applying the current method we are able to predict users' future behavior with a reasonably high accuracy only by having the information of their friendship network. Potential applications include recommendation systems and credit risk forecasting.

arXiv comment6 pages, 4 figures

References in corpus (4)

• Distributed Representations of Words and Phrases and their Compositionality
• Semi-Supervised Classification with Graph Convolutional Networks
• Graph Embedding Techniques, Applications, and Performance: A Survey
• Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts