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.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. 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)在会议建议中取得了最新结果。 在这项工作中,我们探讨了如何将其应用于Booking.com数据挑战竞争。挑战的目的是根据数以百万计的真实匿名住宿预订的数据集,为用户旅行的下一个目的地提出最佳建议。我们在这一竞争中获得了第二位。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,并学习它们的矢量代表。接下来,我们应用EMDE来预测下一个用户目的地,根据以前访问的城市和与每次旅行有关的一些特征。我们发布了源代码:https://github.com/Synerise/booking-challenge。