  Deep learning based methods have been used successfully in recommender system
problems. Approaches using recurrent neural networks, transformers, and
attention mechanisms are useful to model users' long- and short-term
preferences in sequential interactions. To explore different session-based
recommendation solutions, Booking.com recently organized the WSDM WebTour 2021
Challenge, which aims to benchmark models to recommend the final city in a
trip. This study presents our approach to this challenge. We conducted several
experiments to test different state-of-the-art deep learning architectures for
recommender systems. Further, we proposed some changes to Neural Attentive
Recommendation Machine (NARM), adapted its architecture for the challenge
objective, and implemented training approaches that can be used in any
session-based model to improve accuracy. Our experimental result shows that the
improved NARM outperforms all other state-of-the-art benchmark methods.
