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.

翻译：深层次的学习方法已成功地用于推荐系统问题。 使用经常性神经网络、变压器和关注机制的方法有助于在相继互动中模拟用户的长期和短期偏好。 为探索不同基于会议的建议解决方案,Booking.com最近组织了WSDM WebTour 2021挑战,目的是为在一次旅行中推荐最终城市的模型制定基准。本研究介绍了我们应对这一挑战的方法。我们进行了几次实验,以测试推荐系统的各种最先进的深层次学习结构。此外,我们提议对神经惯性建议机器(NARM)进行一些修改,调整其结构以适应挑战目标,并实施了培训方法,这些方法可用于任何基于会议的模式,以提高准确性。我们的实验结果表明,改进的NARM比所有其他最先进的基准方法都好。