Bandit algorithms are a class of machine learning techniques designed to solve decision-making problems under uncertainty. They originate from the “multi-armed bandit” problem, a hypothetical scenario where a gambler must choose between multiple slot machines (bandits) with unknown reward probabilities to maximize earnings. In recommendation systems, bandit algorithms balance exploration (testing new options) and exploitation (using known high-performing options) to optimize user engagement. For example, a streaming service might use them to decide whether to recommend a popular movie (exploit) or a lesser-known title (explore) to gather data on user preferences.
In recommendations, bandit algorithms dynamically adapt based on user feedback. Common approaches include epsilon-greedy, Upper Confidence Bound (UCB), and Thompson Sampling. Epsilon-greedy, for instance, randomly explores new items with a small probability (epsilon) while mostly exploiting known preferences. UCB prioritizes items with high uncertainty by calculating confidence intervals around reward estimates. Thompson Sampling uses probabilistic models to sample potential rewards and select items. For example, an e-commerce platform might use Thompson Sampling to test product recommendations, updating probabilities as users click or purchase items. These methods enable real-time adjustments, unlike static collaborative filtering, which relies on historical data and cannot adapt quickly.
Bandit algorithms offer efficiency in resource allocation and responsiveness but face challenges. They reduce wasted impressions by focusing on high-potential items early, which is useful for cold-start scenarios (e.g., new songs on a music platform). However, scalability can be an issue with large item catalogs, as maintaining reward estimates for millions of items requires significant computation. Tuning parameters like exploration rate (epsilon) or confidence intervals is also critical to avoid over-exploring or stagnating. Despite these challenges, bandit algorithms are widely adopted in recommendation systems for their ability to balance real-time learning with user satisfaction, making them a practical tool for developers aiming to optimize dynamic content delivery.