Metric Learning for Session-based Recommendations
This work addresses the problem of improving session-based recommendations for users by providing a more effective method than existing approaches.
This paper proposes using metric learning for session-based recommendations, creating a common embedding space for sessions and items to measure dissimilarity. The proposed simple architecture outperforms existing methods on four datasets without requiring extensive size or depth.
Session-based recommenders, used for making predictions out of users' uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users' events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study.