AdaTTA is an actor-critic RL framework that selects sequence-specific test-time augmentations and improves recommendation metrics by up to 26% over fixed augmentation strategies on four datasets.
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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.
FLAME condenses ensemble diversity into a single network via modular ensemble simulation and guided mutual learning during training, delivering ensemble-level performance with single-network inference speed on sequential recommendation tasks.
ADS-POI decomposes user mobility sequences into multiple parallel evolving latent sub-states with context-conditioned aggregation to improve next POI recommendation accuracy.
CaST-POI improves next POI recommendation by conditioning user history attention on each candidate and adding candidate-relative temporal and spatial biases.
citing papers explorer
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Beyond One-Size-Fits-All: Adaptive Test-Time Augmentation for Sequential Recommendation
AdaTTA is an actor-critic RL framework that selects sequence-specific test-time augmentations and improves recommendation metrics by up to 26% over fixed augmentation strategies on four datasets.
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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.
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FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation
FLAME condenses ensemble diversity into a single network via modular ensemble simulation and guided mutual learning during training, delivering ensemble-level performance with single-network inference speed on sequential recommendation tasks.
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ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation
ADS-POI decomposes user mobility sequences into multiple parallel evolving latent sub-states with context-conditioned aggregation to improve next POI recommendation accuracy.
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CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation
CaST-POI improves next POI recommendation by conditioning user history attention on each candidate and adding candidate-relative temporal and spatial biases.