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.

翻译：最近提议的EMDE模式(快速管理密度模拟器)在届会建议中取得了最新结果。 在这项工作中,我们探索了该模式在预订数据挑战竞争中的应用。挑战的目的是根据数以百万计的真实匿名住宿保留区数据集,为用户旅行的下一个目的地提出最佳建议。我们在这一竞争中达到了第二位。首先,我们用我们的图表嵌入方法Cleora来代表城市,作为定向图表,学习它们的矢量代表。接下来,我们应用EMDE来预测基于以前访问的城市的下一个用户目的地和与每次访问有关的一些特征。我们发布了源代码,网址是:https://github.com/Synerising/booking-challenge。