Boosting algorithms for a session-based, context-aware recommender system in an online travel domain

Abstract

To keep up with a highly competitive the online hotel booking sector, it is necessary to develop fast and robust recommender systems. The 2019 RecSys Challenge is focused on ways we may use session-based and context-aware signals from users to improve the quality of hotel booking recommendations. In this paper, we present our approach to the challenge. We focus on the proper problem representation, feature extraction, and model blending. Our team achieved the 1st place out of 500 teams in the challenge, with the final MRR score of 0.685711.