Modeling Multi-Destination Trips with Sketch-Based Model
This work addresses trip planning for users in travel recommendation, but it is incremental as it applies an existing method to a new dataset.
The authors tackled the problem of recommending the next destination in multi-destination trips using a sketch-based model, achieving second place in the Booking Data Challenge competition.
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.