Corpus ID: 198120381

Production Ranking Systems: A Review

@article{Iqbal2019ProductionRS,
  title={Production Ranking Systems: A Review},
  author={M. Iqbal and Nishan Subedi and Kamelia Aryafar},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.12372}
}

M. Iqbal, Nishan Subedi, Kamelia Aryafar

Published 2019

Computer Science, Mathematics

ArXiv

The problem of ranking is a multi-billion dollar problem. In this paper we present an overview of several production quality ranking systems. We show that due to conflicting goals of employing the most effective machine learning models and responding to users in real time, ranking systems have evolved into a system of systems, where each subsystem can be viewed as a component layer. We view these layers as being data processing, representation learning, candidate selection and online inference… CONTINUE READING

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