Personalisation in e-commerce hinges on dynamically uncovering the user’s context via his/her interactions on the portal. The harder the context identification, lesser is the effectiveness of personalisation. Our work attempts to uncover and understand the user’s context to effectively render personalisation for fashion ecommerce. We highlight fashion-domain specific gaps with typical implementations of personalised recommendation systems and present an alternate approach. Our approach hinges on user sessions (clickstream) as a proxy to the context and explores “session vector” as an atomic unit for personalization. The approach to learn context vector incorporates both the fashion product (style) attributes and the users’ browsing signals. We establish various possible user contexts (product clusters) and a style can have a fuzzy membership into multiple contexts. We predict the user’s context using the skip-gram model with negative sampling introduced by Mikolov et al [1]. We are able to decode the context with a high accuracy even for non-coherent sessions.