An Event Detection Platform to Detect Gender Using Deep Learning

There are many events that occur in e-commerce platforms, which can be used to detect and understand the behavior of online users. Behavior analyses of e-commerce users can be utilized to impact both customers and businesses. Behavior analysis seeks to find useful information from clickstreams, which can be used to address challenging problems. Clickstreams quantify users' movements based on the items they click on an e-commerce website. This work aims to mine clickstreams to predict users' genders. The proposed approach utilizes deep learning and has been tested on a real-world dataset; the proposed approach outperformed others in terms of accuracy.