DPAA mitigates popularity bias in GNN-based collaborative filtering by integrating adaptive embedding-aware interaction weighting stabilized from pre-trained embeddings and layer-wise amplification of higher-order neighborhoods, outperforming prior debiasing methods on real and semi-synthetic data.
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5 Pith papers cite this work. Polarity classification is still indexing.
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DNNs mitigate dimensional collapse of embeddings in feature interaction models, shown via parallel and stacked experiments plus gradient analysis.
A unified benchmark of eleven CE methods shows effectiveness-sparsity trade-offs vary by method and format, performance is consistent from item to list level, and graph-based explainers face scalability limits on large graphs.
JBM-Diff applies conditional graph diffusion to remove preference-irrelevant multimodal noise and false-positive/negative behaviors, then augments training data via partial-order credibility scoring.
DCGL introduces a dual-channel architecture with multi-level contrastive learning and frequency-adaptive fusion to improve knowledge-aware recommendations, especially in sparse data settings.
citing papers explorer
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Debiasing Message Passing to Mitigate Popularity Bias in GNN-based Collaborative Filtering
DPAA mitigates popularity bias in GNN-based collaborative filtering by integrating adaptive embedding-aware interaction weighting stabilized from pre-trained embeddings and layer-wise amplification of higher-order neighborhoods, outperforming prior debiasing methods on real and semi-synthetic data.
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Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective
DNNs mitigate dimensional collapse of embeddings in feature interaction models, shown via parallel and stacked experiments plus gradient analysis.
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From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
A unified benchmark of eleven CE methods shows effectiveness-sparsity trade-offs vary by method and format, performance is consistent from item to list level, and graph-based explainers face scalability limits on large graphs.
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Joint Behavior-guided and Modality-coherence Conditional Graph Diffusion Denoising for Multi Modal Recommendation
JBM-Diff applies conditional graph diffusion to remove preference-irrelevant multimodal noise and false-positive/negative behaviors, then augments training data via partial-order credibility scoring.
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DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation
DCGL introduces a dual-channel architecture with multi-level contrastive learning and frequency-adaptive fusion to improve knowledge-aware recommendations, especially in sparse data settings.