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 shared entities can inflate connectivity and bias reliability estimates. Here we present AdaptedKG, ...
| Authors | Zichun Jin, Zihan Zhou, Yinan Liu et al. |
|---|---|
| Primary category | cs.IR |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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