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cs.IR2025
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,…
cs.IR2025
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…
cs.IR2023
Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking
Xuewei Wang, Qiang Jin, Shengyu Huang +10
Dividing ads ranking system into retrieval, early, and final stages is a common practice in large scale ads recommendation to balance the efficiency and accuracy. The early stage r…