2019/07/24 by Murium Iqbal, Iqbal, Murium, Nishan Subedi +3
Computer Science · Mathematics · #Advanced Database Systems and Queries #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #cs.IR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1907.12372
SIGIR eComm Accepted Paper
arxiv created 2019/07/24 · openalex publication_date 2019/07/24 · arxiv updated 2019/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. Each layer employs different algorithms and tools, with every end-to-end ranking system spanning multiple architectures. Our goal is to familiarize the general audience with a working knowledge of ranking at scale, the tools and algorithms employed and the challenges introduced by adopting a layered approach.