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TempGNN: Temporal Graph Neural Networks for Dynamic Session-Based Recommendations
Session-based recommendations which predict the next action by understanding a user's interaction behavior with items within a relatively short ongoing session have recently gained increasing popularity.
STING: Self-attention based Time-series Imputation Networks using GAN
Although recent works based on deep neural networks have shown remarkable results, they still have a limitation to capture the complex generation process of the multivariate time series.
SR-GCL: Session-Based Recommendation with Global Context Enhanced Augmentation in Contrastive Learning
As a crucial component of contrastive learning, we propose two global context enhanced data augmentation methods while maintaining the semantics of the original session.
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