QCMP-CL introduces learnable collaborative sequence augmentation from same-target and similar sequences plus a quality-aware weighting mechanism based on augmentation confidence, outperforming prior CL-based sequential recommendation methods on three real-world datasets.
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Yu, Julian McAuley, and Caiming Xiong
15 Pith papers cite this work. Polarity classification is still indexing.
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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.
S2-CAR uses Context-Aware Soft Temporal Point Process for energy-decay segmentation of user sequences followed by segment-count-adaptive multi-intent extraction, reporting consistent gains over 13 baselines on three public datasets.
GenAIR generates LLM-derived archetype embeddings for items and applies behavioral calibration to close the semantic-behavioral gap, yielding performance gains on three real-world datasets when integrated with existing sequential models.
UFRec adaptively weights multi-step future supervision by next-item prediction confidence and adds future-trajectory contrastive learning to improve sequential recommendation under data sparsity.
BDPL improves heterogeneous sequential recommendation by constructing behavior-aware subgraphs, aggregating via cascade GNN, and enhancing representations with preference-level contrastive learning before adaptive fusion for target behavior prediction.
MVCrec improves next-item prediction by running three contrastive losses (within-ID, within-graph, and cross-view) and fusing the resulting representations with global-plus-local attention.
ReAd retrieves collaboratively similar items, builds an augmentation embedding via a lightweight module, and fuses it to refine sequential recommendation predictions, outperforming baselines on five datasets.
Sub-sequence splitting interferes with fair evaluation in sequential recommendation models and enhances performance only when paired with particular splitting, targeting, and loss function choices.
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.
BIPCL improves sequential recommendation accuracy by bilaterally injecting collective intent prototypes into representations and enforcing contrastive alignment via bounded embedding perturbations.
AsarRec learns adaptive sequence augmentations via transformation matrices and Semi-Sinkhorn projection to improve robustness of self-supervised sequential recommenders under noise.
RCL adds similarity-based weak positive samples to supervised contrastive learning in sequential recommendation and reports an average 4.88% improvement over state-of-the-art methods across six datasets.
SAERec extracts fine-grained interpretable intents from LLM embeddings via sparse autoencoders and integrates them as priors into sequence recommendation using multi-branch attention, outperforming baselines on public datasets.
ConvRec applies hierarchical convolutional layers to generate compact sequence representations for attribute-aware sequential recommendation, achieving linear complexity and outperforming attention-based state-of-the-art models on four real-world datasets.
citing papers explorer
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Quality-Aware Collaborative Multi-Positive Contrastive Learning for Sequential Recommendation
QCMP-CL introduces learnable collaborative sequence augmentation from same-target and similar sequences plus a quality-aware weighting mechanism based on augmentation confidence, outperforming prior CL-based sequential recommendation methods on three real-world datasets.
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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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S2-CAR: Segmentation-Supervised Complexity-Adaptive Recommendation
S2-CAR uses Context-Aware Soft Temporal Point Process for energy-decay segmentation of user sequences followed by segment-count-adaptive multi-intent extraction, reporting consistent gains over 13 baselines on three public datasets.
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Generative Archetype-Grounded Item Representations for Sequential Recommendation
GenAIR generates LLM-derived archetype embeddings for items and applies behavioral calibration to close the semantic-behavioral gap, yielding performance gains on three real-world datasets when integrated with existing sequential models.
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Looking Farther with Confidence: Uncertainty-Guided Future Learning for Sequential Recommendation
UFRec adaptively weights multi-step future supervision by next-item prediction confidence and adds future-trajectory contrastive learning to improve sequential recommendation under data sparsity.
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Behavior-Aware Dual-Channel Preference Learning for Heterogeneous Sequential Recommendation
BDPL improves heterogeneous sequential recommendation by constructing behavior-aware subgraphs, aggregating via cascade GNN, and enhancing representations with preference-level contrastive learning before adaptive fusion for target behavior prediction.
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ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation
MVCrec improves next-item prediction by running three contrastive losses (within-ID, within-graph, and cross-view) and fusing the resulting representations with global-plus-local attention.
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Retrieve-then-Adapt: Retrieval-Augmented Test-Time Adaptation for Sequential Recommendation
ReAd retrieves collaboratively similar items, builds an augmentation embedding via a lightweight module, and fuses it to refine sequential recommendation predictions, outperforming baselines on five datasets.
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Pay Attention to Sequence Split: Uncovering the Impacts of Sub-Sequence Splitting on Sequential Recommendation Models
Sub-sequence splitting interferes with fair evaluation in sequential recommendation models and enhances performance only when paired with particular splitting, targeting, and loss function choices.
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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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BIPCL: Bilateral Intent-Enhanced Sequential Recommendation via Embedding Perturbation Contrastive Learning
BIPCL improves sequential recommendation accuracy by bilaterally injecting collective intent prototypes into representations and enforcing contrastive alignment via bounded embedding perturbations.
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AsarRec: Adaptive Sequential Augmentation for Robust Self-supervised Sequential Recommendation
AsarRec learns adaptive sequence augmentations via transformation matrices and Semi-Sinkhorn projection to improve robustness of self-supervised sequential recommenders under noise.
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Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Pair Selection
RCL adds similarity-based weak positive samples to supervised contrastive learning in sequential recommendation and reports an average 4.88% improvement over state-of-the-art methods across six datasets.
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SAERec: Constructing Fine-grained Interpretable Intents Priors via Sparse Autoencoders for Recommendation
SAERec extracts fine-grained interpretable intents from LLM embeddings via sparse autoencoders and integrates them as priors into sequence recommendation using multi-branch attention, outperforming baselines on public datasets.
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Rethinking Convolutional Networks for Attribute-Aware Sequential Recommendation
ConvRec applies hierarchical convolutional layers to generate compact sequence representations for attribute-aware sequential recommendation, achieving linear complexity and outperforming attention-based state-of-the-art models on four real-world datasets.