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This research aims to evaluate the effectiveness of various bandit algorithms in optimizing digital advertising strategies.

January 25, 2026

Bandit Algorithms for Advertising Optimization: A Comparative Study

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This research aims to evaluate the effectiveness of various bandit algorithms in optimizing digital advertising strategies.
Explored different bandit algorithms: ε-greedy, UCB, LinUCB, and Softmax.
Compared performance based on cumulative return and accuracy in advertising recommendations.
Utilized a static experimental dataset to assess algorithm performance.
LinUCB outperformed other algorithms in terms of cumulative return and prediction accuracy.
Incorporating user characteristics significantly improved relevance of advertising recommendations.
Static dataset limitations indicate challenges in simulating dynamic real-time environments.

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Comparative study reveals that bandit algorithms improve advertising effectiveness, underscoring adaptive strategies.

Ziyue Tian (2025) studied this question.

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