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Found 2 papers, 2 papers with code
Transition Information Enhanced Disentangled Graph Neural Networks for Session-based Recommendation
The SOTA methods in SBR employ GNN to model neighboring item transitions from global (i. e, other sessions) and local (i. e, current session) contexts.
Disentangled Graph Neural Networks for Session-based Recommendation
The session embedding is then generated by aggregating the item embeddings with attention weights of each item's factors.
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