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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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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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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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.