TIDE disentangles habitual repurchase from exploratory interest in next-basket recommendation using Hawkes-enhanced Fourier time encoding, dual experts, and item-aware gating, outperforming prior methods on four datasets.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
Time-Interval-Aware Disentangled Expert Modeling for Next-Basket Recommendation
TIDE disentangles habitual repurchase from exploratory interest in next-basket recommendation using Hawkes-enhanced Fourier time encoding, dual experts, and item-aware gating, outperforming prior methods on four datasets.
-
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.