12 papers
MISO: Model-Internal-State-Guided Optimization for Ranking Models
Yongzhe Zhang, Xiaoyu Deng, Yifan He +29
Ranking models are repeatedly refined within established model families, yet the choice of which component to scale, replace, or retire is often guided by expensive trial-and-error…
MARCO: Click-Intent Decomposition for Calibrated Ads Conversion Prediction
Shiwen Shen, Xiru Huang, Liang Luo +32
Not all clicks are equal. Industrial ads ranking decouples conversion probability into click-through rate (CTR) and post-click conversion rate (CVR), yet treats every click as the…
ROCS: Request-Oriented Compute Sharing for Efficient Large-Scale Recommendation
Yuxin Chen, Liang Luo, Buyun Zhang +44
Modern recommendation models gain prediction quality by scaling feature-interaction and sequence modules, but production cost constraints cap how far systems can scale. In this wor…
GR2 Technical Report
Yufei Li, Zaiwei Zhang, Mingfu Liang +67
Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step dispropo…
LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation
Shali Jiang, Hua Zheng, Boyang Liu +46
Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from diminishing transfer ratio -- t…
LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
Liang Luo, Yinbin Ma, Quanyu Zhu +21
Recent GPU generations deliver significantly higher FLOPs using lower-precision arithmetic, such as FP8. While successfully applied to large language models (LLMs), its adoption in…
Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design
Bojian Hou, Xiaolong Liu, Xiaoyi Liu +26
Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-s…
GR2: Generative Reasoning Re-ranker
Mingfu Liang, Yufei Li, Jay Xu +20
Recent studies increasingly explore Large Language Models (LLMs) as a new paradigm for recommendation systems due to their scalability and world knowledge. However, existing work h…
Meta Lattice: Model Space Redesign for Cost-Effective Industry-Scale Ads Recommendations
Liang Luo, Yuxin Chen, Zhengyu Zhang +39
The rapidly evolving landscape of products, surfaces, policies, and regulations poses significant challenges for deploying state-of-the-art recommendation models at industry scale,…
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
Mingfu Liang, Xi Liu, Rong Jin +104
Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommenda…
Towards Data Quality Assessment in Online Advertising
Sahin Cem Geyik, Jianqiang Shen, Shahriar Shariat +2
In online advertising, our aim is to match the advertisers with the most relevant users to optimize the campaign performance. In the pursuit of achieving this goal, multiple data s…
Scalable Audience Reach Estimation in Real-time Online Advertising
Ali Jalali, Santanu Kolay, Peter Foldes +1
Online advertising has been introduced as one of the most efficient methods of advertising throughout the recent years. Yet, advertisers are concerned about the efficiency of their…