Hybrid Model with Time Modeling for Sequential Recommender Systems

• URL: http://arxiv.org/abs/2103.06138v1
• Date: Sun, 7 Mar 2021 19:28:22 GMT
• Title: Hybrid Model with Time Modeling for Sequential Recommender Systems
• Authors: Marlesson R. O. Santana, Anderson Soares
• Abstract summary: Booking.com organized the WSDM WebTour 2021 Challenge, which aims to benchmark models to recommend the final city in a trip. We conducted several experiments to test different state-of-the-art deep learning architectures for recommender systems. Our experimental result shows that the improved NARM outperforms all other state-of-the-art benchmark methods.
• Score: 0.15229257192293202
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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