Bandit algorithms are a type of machine learning method designed to make decisions in uncertain situations where outcomes are unknown. The name "bandit" comes from the multi-armed bandit problem, which is a classic example in probability theory. In this scenario, a gambler must decide which slot machine (armed) to play without knowing which will provide the highest reward. Bandit algorithms balance the exploration of new options and the exploitation of known profitable ones, thus maximizing the overall reward over time.
In the context of recommendations, bandit algorithms are particularly useful because they can adapt to user preferences dynamically. For example, when a user interacts with a recommendation system—such as an e-commerce site suggesting products or a streaming service showcasing movies—the bandit algorithm evaluates how well certain recommendations perform based on user feedback or engagement. If a user often watches action movies, the system can quickly learn to suggest similar content. In contrast, the algorithm can also explore options outside the user’s established preferences to refine its recommendations continuously.
A common implementation of bandit algorithms in recommendations is the epsilon-greedy strategy. This approach allows the algorithm to explore with a small probability (epsilon) by suggesting random items, while predominantly recommending high-performing items based on past interactions. Another example is the Thompson Sampling method, which uses Bayesian principles to provide recommendations based on the likelihood of different options being successful. Both methods effectively personalize user experiences, improve engagement, and ultimately lead to higher satisfaction with the recommendations provided.