Customer gender prediction system on hierarchical E-commerce data

Background E-commerce services provide online shopping sites and mobile applications for small and medium sellers. To provide more efficient buying and selling experiences, a machine learning system can be applied to predict the optimal organization and display of products that maximize the chance of bringing useful information to user that facilitate the online purchases. Therefore, it is important to understand the relevant products for a gender to facilitate the online purchases. In this work, we present a statistical machine learning (ML)-based gender prediction system to predict the gender “male” or “female” from transactional E-commerce data. We introduce different sets of learning algorithms including unique IDs decomposition, context window-based history generation, and extract identical hierarchy from training set to address the gender prediction classification system from online transnational data. Results The experiment result shows that different feature augmentation approaches as well as different term or feature weighting approaches can significantly enhance the performance of statistical machine learning-based gender prediction system. Conclusions This work presents a ML-based implementational approach to address E-commerce-based gender prediction system. Different session augmentation approaches with support vector machines (SVMs) classifier can significantly improve the performance of gender prediction system.