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.

本文介绍了基于深度学习的推荐系统方法，特别是基于循环神经网络、transformers和attention机制的方法。通过多次实验和改进Neural Attentive Recommendation Machine (NARM)，我们的研究表明优化后的NARM是所有现有基准方法中表现最好的。

基于时间建模的混合模型用于顺序推荐系统