Скачать или смотреть Introduction to Multi-Arm Bandit problem in AI Marketing

The Multi-Armed Bandit (MAB) is a statistical framework for solving the dilemma of allocating resources across multiple options, like ad creatives, to maximize total reward. This analogy compares the marketing ecosystem to a casino, where each ad is a "bandit" with a hidden probability of conversion (reward). MAB algorithms dynamically manage the core conflict between *Exploitation* (concentrating budget on the current best performer) and *Exploration* (investing budget to gather data). By learning while earning, MAB moves campaigns from guesswork to dynamic, self-optimizing systems that maximize ROI in real-time.
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