Contextual Bandit

Name: WatEase
Author: WatEase

A multi-armed bandit that conditions arm choice on observable context features (time, audience, device).

Last updated: 2026-05-04

Definition

Vanilla MAB picks the same arm regardless of who's looking. Contextual MAB conditions on context: 'the best creative for a Mumbai user on iOS at 8 PM might differ from a Bangalore user on Android at 10 AM.' Used in WatEase's journey orchestrator to personalize variant selection per audience cohort.

How it applies in India

No India-specific behavior.

Related terms

Thompson SamplingA Bayesian multi-armed bandit algorithm that chooses arms in proportion to their probability of being optimal.
Multi-Armed BanditA class of algorithms (Thompson, UCB, ε-greedy) that balance exploring uncertain options against exploiting known winners.

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