Modeling Multi-Destination Trips with Sketch-Based Model

TL;DR

This paper applies the EMDE model to the Booking Data Challenge, using graph embeddings and density estimation to predict user trip destinations, achieving second place in the competition.

Contribution

It demonstrates the effective adaptation of EMDE for multi-destination trip modeling using graph embeddings and feature-based prediction.

Findings

Achieved 2nd place in the Booking Data Challenge

Successfully integrated graph embeddings with EMDE for destination prediction

Provided open-source code for reproducibility

Abstract

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.