  Providing a personalized user experience on information dense webpages helps
users in reaching their end-goals sooner. We explore an automated approach to
identifying user personas by leveraging high dimensional trajectory information
from user sessions on webpages. While neural collaborative filtering (NCF)
approaches pay little attention to token semantics, our method introduces
SessionBERT, a Transformer-backed language model trained from scratch on the
masked language modeling (mlm) objective for user trajectories (pages,
metadata, billing in a session) aiming to capture semantics within them. Our
results show that representations learned through SessionBERT are able to
consistently outperform a BERT-base model providing a 3% and 1% relative
improvement in F1-score for predicting page links and next services. We
leverage SessionBERT and extend it to provide recommendations (top-5) for the
next most-relevant services that a user would be likely to use. We achieve a
HIT@5 of 58% from our recommendation model.
