Year: 2018
Type: preprint
Abstract: In this paper, we present an architecture executing a complex machine learning model such as a neural network capturing semantic similarity between a query and a document; and deploy to a real-world production system serving 500M+users. We present the challenges that arise in a real-world system and how we solve them. We demonstrate that our architecture provides competitive modeling capability without any significant performance impact to the system in terms of latency. Our modular solution and insights can be used by other real-world search systems to realize and productionize recent gains in neural networks.
Source: arXiv (Cornell University)
Authors: Ramanath, Rohan, Polatkan, Gungor, Xu, Liqin, Lee, Harold, Hu, Bo, Zhou, Shan
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2806615039