6 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…
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…
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…
Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model
Yu Xia, Rui Zhong, Hao Gu +4
Large Language Models (LLMs) have garnered significant attention in Recommendation Systems (RS) due to their extensive world knowledge and robust reasoning capabilities. However, a…
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…
LDACP: Long-Delayed Ad Conversions Prediction Model for Bidding Strategy
Peng Cui, Yiming Yang, Fusheng Jin +8
In online advertising, once an ad campaign is deployed, the automated bidding system dynamically adjusts the bidding strategy to optimize Cost Per Action (CPA) based on the number…