11 citations · 12 across the 13 of their papers we have counts for
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cs.IR2023
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…
cs.LG2023
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
Wei Wen, Kuang-Hung Liu, Igor Fedorov +19
Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are eva…
cs.IR2023★ 11 cited
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…