SUIN improves CTR prediction by augmenting target user sequences with similar users' behaviors via embedding-based retrieval, user-specific position encoding, and user-aware target attention.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 6verdicts
UNVERDICTED 6roles
background 2polarities
background 2representative citing papers
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.
DNNs mitigate dimensional collapse of embeddings in feature interaction models, shown via parallel and stacked experiments plus gradient analysis.
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.
DIAURec unifies intent and language modeling to reconstruct and optimize representations in prototype and distribution spaces, outperforming baselines on three datasets.
citing papers explorer
-
Similar Users-Augmented Interest Network
SUIN improves CTR prediction by augmenting target user sequences with similar users' behaviors via embedding-based retrieval, user-specific position encoding, and user-aware target attention.
-
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.
-
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.
-
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.
-
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.
-
DIAURec: Dual-Intent Space Representation Optimization for Recommendation
DIAURec unifies intent and language modeling to reconstruct and optimize representations in prototype and distribution spaces, outperforming baselines on three datasets.