Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation
Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combine persistent preferences with temporary needs, exploration, and incidental behavior, so some interactions can distort history representations or provide unreliable supervision. Existing denoising methods judge such interactions mainly from co-occurrence, order, or model predictions, without explicit evidence from relations between items. Knowledge graphs (KGs) offer this evidence, but item popularity, graph degree, uneven coverage, and widely sha
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Zichun Jin →
“Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation”
- LinkedLinked via arxiv author · 85%Zihan Zhou →
“Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation”
- LinkedLinked via arxiv author · 85%Yinan Liu →
“Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation”
- LinkedLinked via arxiv author · 85%Bin Wang →
“Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation”
- LinkedLinked via arxiv author · 85%Xiaochun Yang →
“Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation”
- FuzzyOverlapping authors or contributors · 62%keras-team/keras →
“Shared author/contributor keys: jin”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%HKUDS/LightRAG →
“Shared author/contributor keys: jin”