An attentive RNN model for session-based and context-aware recommendations: a solution to the RecSys challenge 2019

In the RecSys Challenge 2019 the participants were asked to predict which items, from a presented list of items/accommodations of a search result on trivago, had been clicked-on during the last part of a user's session. Here we present the 7th place solution1. It consists of a neural network designed to learn interactions between session, context, sequence features, and the features of the displayed items at the time of a click. Our approach uses well established deep learning techniques, such as Recurrent Neural Networks, Attention and self-Attention mechanisms to deal with the different aspects of the information available, and it predicts a (categorical) probability distribution over the list of presented items. In addition to the model structure we also describe the somewhat heavy feature engineering, data augmentation and other decisions/observations made a long the way.