Publication Type

Journal Article

Publication Date

1-2022

Abstract

Predicting consumers’ purchasing behaviors is critical for targeted advertisement and sales promotion in e-commerce. Human faces are an invaluable source of information for gaining insights into consumer personality and behavioral traits. However, consumer's faces are largely unexplored in previous research, and the existing face-related studies focus on high-level features such as personality traits while neglecting the business significance of learning from facial data. We propose to predict consumers’ purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-N purchase destinations of a consumer. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers’ purchasing behaviors.

Keywords

Correlation analysis, Graphical neural networks, Hierarchical embedding, Purchase prediction

Discipline

Databases and Information Systems | E-Commerce | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Knowledge-Based Systems

Volume

235

First Page

1

Last Page

10

ISSN

0950-7051

Identifier

10.1016/j.knosys.2021.107665

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.knosys.2021.107665

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