Predicting online user behaviour using deep learning algorithms

Predicting user intentionality towards a certain product, or category, based on interactions within a website is crucial for e-commerce sites and ad display networks, especially for retargeting. By keeping track of the search patterns of the consumers, online merchants can have a better understanding of their behaviours and intentions [4]. In mobile e-commerce a rich set of data is available and potential consumers search for product information before making purchasing decisions, thus reflecting consumers purchase intentions. Users show different search patterns, i.e, time spent per item, search frequency and returning visits [1]. 1 2 DATA DESCRIPTION Clickstream data can be used to quantify search behavior using machine learning techniques [5], mostly focused on purchase records. While purchasing indicates consumers final preferences in the same category, search is also an essential component to measure intentionality towards a specific category.