1 citations · 1 across the 5 of their papers we have counts for
5 papers
Beyond Semantic IDs: Encoding Business-Value Ranking into Document Identifiers for Generative Retrieval
Gui Ling, Zhihong Chen, Yu Li +7
Generative Retrieval (GR) formulates retrieval as a sequence-to-sequence generation task, assigning each document a document identifier (DocID) and retrieving it through autoregres…
Differentiable Fast Top-K Selection for Large-Scale Recommendation
Yanjie Zhu, Zhen Zhang, Yunli Wang +7
Cascade ranking is a widely adopted paradigm in large-scale information retrieval systems for Top-K item selection. However, the Top-K operator is non-differentiable, hindering end…
FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets
Kairui Fu, Tao Zhang, Shuwen Xiao +9
Semantic identifiers (SIDs) have gained increasing attention in generative retrieval (GR) for recommendation due to their meaningful semantic discriminability. However, current stu…
Learning Cascade Ranking as One Network
Yunli Wang, Zhen Zhang, Zhiqiang Wang +6
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage…
Adaptive: Adaptive Domain Mining for Fine-grained Domain Adaptation Modeling
Wenxuan Sun, Zixuan Yang, Yunli Wang +8
Advertising systems often face the multi-domain challenge, where data distributions vary significantly across scenarios. Existing domain adaptation methods primarily focus on build…