Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking
Kuaishou serves over 400 million daily active users, processing hundreds of millions of search queries daily against a repository of tens of billions of short videos. As the final decision layer, the reranking stage determines user experience by optimizing whole-page utility. While traditional score-and-sort methods fail to capture combinatorial dependencies, Generative Reranking offers a superior paradigm by directly modeling the permutation probability. However, deploying Generative Reranking in such a high-stakes environment faces a fundamental dual dilemma: 1) the structural trade-off where Autoregressive (AR) models offer superior Sequential modeling but suffer from prohibitive laten...
| Authors | Chao Zhang, Shuai Lin, ChengLei Dai et al. |
|---|---|
| Primary category | cs.IR |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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