Kun Gai
4 papers · Latest:
UniRank: Unified List-wise Reranking via Confidence-Ordered Denoising
UniRank unifies autoregressive and non-autoregressive reranking using confidence-ordered denoising, improving performance and user engagement.
Break the Inaccessible Boundary: Distilling Post-Conversion Content for User Retention Modeling
OCARM uses a two-stage distillation framework to leverage post-conversion content for improved user retention prediction in real-time bidding without feature leakage.
From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space
GloRank introduces a generative reranking framework for recommender systems that uses global item identifiers instead of local indices, improving item understanding and performance.
Kwai Summary Attention Technical Report
Kwai Summary Attention (KSA) reduces LLM long-context modeling costs by compressing historical contexts into learnable summary tokens.
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