CanniUplift mitigates cross-seller substitution and non-redemption noise in e-commerce uplift modeling via a global GMV alignment loss and a redemption-based outcome decomposition, improving platform-wide incremental GMV.
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MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
SSR uses static random filters and iterative competitive sparse mechanisms to explicitly enforce sparsity in recommendation models, outperforming dense baselines on public and billion-scale industrial datasets.
citing papers explorer
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CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling
CanniUplift mitigates cross-seller substitution and non-redemption noise in e-commerce uplift modeling via a global GMV alignment loss and a redemption-based outcome decomposition, improving platform-wide incremental GMV.
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Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
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Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation
SSR uses static random filters and iterative competitive sparse mechanisms to explicitly enforce sparsity in recommendation models, outperforming dense baselines on public and billion-scale industrial datasets.
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