Table of Contents 1. Introduction to Bandit Personalization 2. Core Bandit Algorithms 3. Code Implementations 4. Algorithm Comparison 5. Extended Algorithm Landscape 6. Technical Deep Dive Introduction to Bandit Personalization Bandit personalization refers to using multi-armed bandit algorithms to deliver personalized experiences to users in software applications. It's a machine learning approach that solves the classic "exploration vs exploitation" problem in personalization. How it Works The algorithm treats each personalization option (like different content recommendations, UI layouts, or product suggestions) as an "arm" of a slot machine. For each user interaction, it must decide whether to: Exploit : Show the option that currently appears to work best for that user Explore : Try a different option to potentially discover something even better Key Advantages Benefits over Traditional A/B Testing Real-time learning : Unlik...