11 citations · 11 across the 4 of their papers we have counts for
4 papers
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…
ERCache: An Efficient and Reliable Caching Framework for Large-Scale User Representations in Meta's Ads System
Fang Zhou, Yaning Huang, Dong Liang +21
The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and s…
AutoML for Large Capacity Modeling of Meta's Ranking Systems
Hang Yin, Kuang-Hung Liu, Mengying Sun +16
Web-scale ranking systems at Meta serving billions of users is complex. Improving ranking models is essential but engineering heavy. Automated Machine Learning (AutoML) can release…
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
Wei Zhang, Dai Li, Chen Liang +17
Effective user representations are pivotal in personalized advertising. However, stringent constraints on training throughput, serving latency, and memory, often limit the complexi…