Customer Gender Prediction Model based on E-Commerce Data
Keywords:
Data analytics, Machine learning, Predictive modelling, Decision-making, Advanced analytics, Data management, Data forecast, Business Value, Big dataAbstract
Demographic data of Customers such as gender, age, etc. provide valuable information for marketing and personalization of web applications for e-commerce service providers. However, online consumers often do not supply this sort of information because of privacy concerns and other safety reasons. In this article, we propose a method for gender prediction based on their catalogue browsing data on e-commerce systems, including the date and time of access, list of categories and items displayed, etc. We use a machine learning approach and explore many features derived from catalogue viewing data to predict the sex of viewers. Experiments on PAKDD Data Mining Competition datasets were carried out and the successful results were achieved. Results 92.3% for balanced accuracy and 91.2% for macro F1 indicate that fundamental features such as time viewing, product/category features used in combination with advanced features such as product/category sequence and transfers effectively promote customers' prediction.
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