Phương pháp biểu diễn cây cho dự đoán giới tính khách hàng dựa trên dữ liệu thương mại điện tử
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. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems. 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 81.9% on balanced accuracy and 82.3% 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.