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Found 2 papers, 2 papers with code
Graph Transformer for Recommendation
This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture.
Graph Masked Autoencoder for Sequential Recommendation
While some powerful neural network architectures (e. g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios.
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