A data scientist is tasked with maximizing the click-through rate (CTR) for five different versions of a website's call-to-action button during a live promotional campaign. The system needs to dynamically allocate more user traffic to the button versions that demonstrate higher engagement while simultaneously continuing to test underperforming versions to gather sufficient data. This approach is intended to maximize the total number of clicks over the campaign's duration. Which unconstrained optimization concept is best suited to address this exploration-exploitation problem?
The correct answer is the multi-armed bandit (MAB) problem. This concept directly models the exploration-exploitation tradeoff, which is central to the scenario. The goal is to maximize a reward (clicks) by choosing among multiple options (button versions) with unknown performance. The MAB approach dynamically allocates resources, favoring better-performing options (exploitation) while still gathering data on other options (exploration).
Finding local minima using gradient descent is incorrect. Gradient descent is an iterative algorithm used for finding the minimum of a continuous and differentiable function. It is not designed for problems involving discrete choices with unknown reward probabilities.
The traveling salesman problem is incorrect because it is a constrained optimization problem focused on finding the shortest possible route that visits a set of locations exactly once. This is fundamentally different from choosing the best among a set of independent options.
The one-armed bandit problem is an incorrect, though related, concept. It typically involves a single option with an unknown payout (e.g., to play or not to play). The scenario described involves choosing among multiple competing options, which is the defining characteristic of the multi-armed bandit problem.
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