Gender Prediction of Consumers Using Offline Purchase Data

DOI

10.2991/macmc-17.2018.59How to use a DOI?

Keywords

gender prediction, performance study, offline purchase data

Abstract

Demographic attributes such as gender of consumers provide important in-formation for marketing, personalization, and user behavior research. With the growing necessity for gender information in personalized intelligent systems, gender prediction of consumers has become an important research issue. This paper addresses the problem of predicting consumers' gender based on purchase data and some external factors, such as weather and name. According to the characteristics of offline data, we compared the performance of different algorithms and the contribution of different features to gender prediction was compared. From the experiments conducted on real-world datasets, we found the most important features and the best performing algorithms that influenced the gender prediction of offline purchase da-ta. This study provided suggestions for apparel offline markets to develop effective marketing strategies to reach their target market, for consumer educators and for educators in the retail merchandizing area to prepare their students for future careers.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Cong Wang
AU  - Yang Ji
PY  - 2018/01
DA  - 2018/01
TI  - Gender Prediction of Consumers Using Offline Purchase Data
BT  - Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)
PB  - Atlantis Press
SP  - 281
EP  - 290
SN  - 2352-5401
UR  - https://doi.org/10.2991/macmc-17.2018.59
DO  - 10.2991/macmc-17.2018.59
ID  - Wang2018/01
ER  -