Contextualisation of eCommerce Users
This work addresses the challenge of understanding user behavior in e-commerce, though it appears incremental as it adapts existing NLP methods to this domain.
The authors tackled the problem of modeling consumer intent in e-commerce by developing a scalable framework that treats user session journeys as documents and applies NLP embeddings to capture contextual relationships between pages and visit topics. They empirically validated the consistency and stability of their framework.
A scaleable modelling framework for the consumer intent within the setting of e-Commerce is presented. The methodology applies contextualisation through embeddings borrowed from Natural Language Processing. By considering the user session journeys throughough the pages of a website as documents, we capture contextual relationships between pages, as well as the topics of the of user visits. Finally, we empirically study the consistency and the stability of the presented framework.