  Knowing if a user is a buyer vs window shopper solely based on clickstream
data is of crucial importance for ecommerce platforms seeking to implement
real-time accurate NBA (next best action) policies. However, due to the low
frequency of conversion events and the noisiness of browsing data, classifying
user sessions is very challenging. In this paper, we address the clickstream
classification problem in the fashion industry and present three major
contributions to the burgeoning field of AI in fashion: first, we collected,
normalized and prepared a novel dataset of live shopping sessions from a major
European e-commerce fashion website; second, we use the dataset to test in a
controlled environment strong baselines and SOTA models from the literature;
finally, we propose a new discriminative neural model that outperforms neural
architectures recently proposed at Rakuten labs.
