6 papers
UniScale: Synergistic Entire Space Data and Model Scaling for Search Ranking
Liren Yu, Caiyuan Li, Feiyi Dong +5
Recent advances in Large Language Models (LLMs) have inspired a surge of scaling research in industrial search, advertising, and recommendation systems. However, existing approache…
LERA: LLM-Enhanced RAG for Ad Auction in Generative Chatbots
Haoran Sun, Xinrui Song, Xinyu Zhang +7
The integration of advertising auction mechanisms into large language model (LLM)-based chatbots presents a significant opportunity for commercialization, yet poses unique challeng…
LLM-Auction: Generative Auction towards LLM-Native Advertising
Chujie Zhao, Qun Hu, Shiping Song +4
The commercialization of LLM applications is the next frontier in online advertising, with LLM-native advertising emerging as a promising paradigm by integrating ads into LLM-gener…
Modeling Cascaded Delay Feedback for Online Net Conversion Rate Prediction: Benchmark, Insights and Solutions
Mingxuan Luo, Guipeng Xv, Sishuo Chen +8
In industrial recommender systems, conversion rate (CVR) is widely used for traffic allocation, but it fails to fully reflect recommendation effectiveness because it ignores refund…
MUSE: A Simple Yet Effective Multimodal Search-Based Framework for Lifelong User Interest Modeling
Bin Wu, Feifan Yang, Zhangming Chan +8
Lifelong user interest modeling is crucial for industrial recommender systems, yet existing approaches rely predominantly on ID-based features, suffering from poor generalization o…
AIF: Asynchronous Inference Framework for Cost-Effective Pre-Ranking
Zhi Kou, Xiang-Rong Sheng, Shuguang Han +5
In industrial recommendation systems, pre-ranking models based on deep neural networks (DNNs) commonly adopt a sequential execution framework: feature fetching and model forward co…