  The recently proposed EMDE (Efficient Manifold Density Estimator) model
achieves state of-the-art results in session-based recommendation. In this work
we explore its application to Booking Data Challenge competition. The aim of
the challenge is to make the best recommendation for the next destination of a
user trip, based on dataset with millions of real anonymized accommodation
reservations. We achieve 2nd place in this competition. First, we use Cleora -
our graph embedding method - to represent cities as a directed graph and learn
their vector representation. Next, we apply EMDE to predict the next user
destination based on previously visited cities and some features associated
with each trip. We release the source code at:
https://github.com/Synerise/booking-challenge.
