Synerise at Booking.Com Data Challenge 2021: Modeling Multi-Destination Trips with Sketch-Based Model.

Abstract

Our 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.com 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 - just after NVIDIA team and beating Amazon & Baidu teams, among many others. 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.

Date
Jun 18, 2021
Michal Daniluk
Michal Daniluk
Research Scientist

My research interests include graph representation learning, recommendation systems, behavioral user representations, NLP.