Modeling Multi-Destination Trips with Sketch-Based Model
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 22 Feb 2021.
Builds on
16 of 27 references.
References · 16
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4 more
- 2020An efficient manifold density estimator for all recommendation systemscited by 8
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Citing papers per year: 2021: 1.
1 paper.