6 papers
Efficient LLM-based Advertising via Model Compression and Parallel Verification
Wenxin Dong, Chang Gao, Guanghui Yu +9
Large language models (LLMs) have shown remarkable potential in advertising scenarios such as ad creative generation and targeted advertising. However, deploying LLMs in real-time…
Ada-MK: Adaptive MegaKernel Optimization via Automated DAG-based Search for LLM Inference
Wenxin Dong, Mingqing Hu, Guanghui Yu +7
When large language models (LLMs) serve real-time inference in commercial online advertising systems, end-to-end latency must be strictly bounded to the millisecond range. Yet ever…
GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm
Shaopeng Chen, Chuyue Xie, Huimin Ren +11
Traditional Deep Learning Recommendation Models (DLRMs) face increasing bottlenecks in performance and efficiency, often struggling with generalization and long-sequence modeling.…
Vertical Federated Learning Hybrid Local Pre-training
Wenguo Li, Xinling Guo, Xu Jiao +3
Vertical Federated Learning (VFL), which has a broad range of real-world applications, has received much attention in both academia and industry. Enterprises aspire to exploit more…
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems
Weijie Zhao, Xuewu Jiao, Xinsheng Luo +3
Deep learning has been widely deployed for online ads systems to predict Click-Through Rate (CTR). Machine learning researchers and practitioners frequently retrain CTR models to t…
Communication-Efficient TeraByte-Scale Model Training Framework for Online Advertising
Weijie Zhao, Xuewu Jiao, Mingqing Hu +3
Click-Through Rate (CTR) prediction is a crucial component in the online advertising industry. In order to produce a personalized CTR prediction, an industry-level CTR prediction m…