GraphSW reduces training cost for GNN-based recommender models by selectively accessing graph features.
problem Training GNN-based recommender models on full KG is computationally expensive and impractical.
method GraphSW uses stage-wise training to access a subset of entities in the KG, gradually learning from higher-order features.
result GraphSW improves recommendation performance and helps models converge effectively in high-order features.