4 papers
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…
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…
Scaling Laws for Online Advertisement Retrieval
Yunli Wang, Zhen Zhang, Zixuan Yang +9
The scaling law is a notable property of neural network models and has significantly propelled the development of large language models. Scaling laws hold great promise in guiding…