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Found 4 papers, 2 papers with code
Toward a Better Understanding of Loss Functions for Collaborative Filtering
Inspired by this analysis, we propose a novel loss function that improves the design of alignment and uniformity considering the unique patterns of datasets called Margin-aware Alignment and Weighted Uniformity (MAWU).
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness
Experiments show that the ranking of nodes by scores using GRAPH-wGD is highly correlated with true bridgeness scores.
Incorporating Experts' Judgment into Machine Learning Models
However, in some cases, domain experts might have a judgment about the expected outcome that might conflict with the prediction of ML models.
Dual Policy Learning for Aggregation Optimization in Graph Neural Network-based Recommender Systems
This framework adaptively determines high-order connectivity to aggregate users and items using dual policy learning.
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