Making Contextual Decisions with Low Technical Debt
release_6aafbkrqaregbhykmw525o4lqq
by
Alekh Agarwal, Sarah Bird, Markus Cozowicz, Luong Hoang, John
Langford, Stephen Lee, Jiaji Li, Dan Melamed, Gal Oshri, Oswaldo Ribas,
Siddhartha Sen, Alex Slivkins
2017
Abstract
Applications and systems are constantly faced with decisions that require
picking from a set of actions based on contextual information.
Reinforcement-based learning algorithms such as contextual bandits can be very
effective in these settings, but applying them in practice is fraught with
technical debt, and no general system exists that supports them completely. We
address this and create the first general system for contextual learning,
called the Decision Service.
Existing systems often suffer from technical debt that arises from issues
like incorrect data collection and weak debuggability, issues we systematically
address through our ML methodology and system abstractions. The Decision
Service enables all aspects of contextual bandit learning using four system
abstractions which connect together in a loop: explore (the decision space),
log, learn, and deploy. Notably, our new explore and log abstractions ensure
the system produces correct, unbiased data, which our learner uses for online
learning and to enable real-time safeguards, all in a fully reproducible
manner.
The Decision Service has a simple user interface and works with a variety of
applications: we present two live production deployments for content
recommendation that achieved click-through improvements of 25-30%, another with
18% revenue lift in the landing page, and ongoing applications in tech support
and machine failure handling. The service makes real-time decisions and learns
continuously and scalably, while significantly lowering technical debt.
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