DC4SR improves sequential recommendation denoising by iteratively calibrating LLM semantic priors and model learning posteriors using their disagreement as a signal for better alignment with true user interests.
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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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Disagreement as Signals: Dual-view Calibration for Sequential Recommendation Denoising
DC4SR improves sequential recommendation denoising by iteratively calibrating LLM semantic priors and model learning posteriors using their disagreement as a signal for better alignment with true user interests.
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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.