Customer gender prediction based on E commerce data
Demographic attributes of customers such as gender, age, etc. provide important information for e commerce service providers in marketing and personalization of web applications. However, online customers often do not provide this kind of information due to privacy issues and other security related reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e commerce systems, such as the date and time of access, list of categories and products viewed, etc. We employ a machine learning approach and investigate a number of features derived from catalog viewing information to predict the gender of viewers. Experiments were conducted on datasets provided by the PAKDD'15 Data Mining Competition and achieved the good result. The results 81.2% on balanced accuracy and 81.4% on macro F1 score showed that basic features such as viewing time, products/categories features used together with more advanced features such as products/categories sequence and transfer features effectively facilitate gender prediction of customers.
2016 Eighth International Conference on Knowledge and Systems Engineering (KSE)