The context is any historical or current information you have about the user, such as previously visited pages, past purchase information, device information or geolocation. In website optimization, contextual bandits rely on incoming user context data as it can be used to help make better algorithmic decisions in real time.įor example, you can use a contextual bandit to select a piece of content or ad to display on your website to optimize for click-through rate. This information is the ‘contextual bandit,’ the context and environment in which the experiment occurs. In a real-world scenario, we sometimes have data that can help inform decision-making when choosing between the various actions in a multi-armed bandit situation. With this randomized probability matching strategy, the number of pulls for a given lever should match its actual probability of being the optimal lever. This strategy is based on the Optimism in the Face of Uncertainty principle, and assumes that the unknown mean payoffs of each arm will be as high as possible, based on observable data. The other 1-ε of the time, the arm with highest known payout is pulled. A randomly chosen arm is pulled a fraction ε of the time. (In ‘greedy’ experiments, the lever with highest known payout is always pulled except when a random action is taken). This is an algorithm for continuously balancing exploration with exploitation. Below is a list of some of the most commonly used multi-armed bandit solutions: There are many different solutions that computer scientists have developed to tackle the multi-armed bandit problem. The website needs to make a series of decisions, each with unknown outcome and ‘payout.’ Multi-armed bandit solutions Which ads will drive maximum revenue for their news site? Similar to the issue with news articles, they typically have a large number of ads from which to choose. In this case, they want to maximize advertising revenue, but they may be lacking enough information about the visitor to pursue a specific advertising strategy. The news website has a similar problem in choosing which ads to display to its visitors. The first question is, which articles will get the most clicks? And in which order should they appear? The website’s goal is to maximize engagement, but they have many pieces of content from which to choose, and they lack data that would help them to pursue a specific strategy. With no information about the visitor, all click outcomes are unknown. One real-world example of a multi-armed bandit problem is when a news website has to make a decision about which articles to display to a visitor. This is the “multi-armed bandit problem.” Multi-armed bandit examples At the beginning of the experiment, when odds and payouts are unknown, the gambler must determine which machine to pull, in which order and how many times. The goal is to determine the best or most profitable outcome through a series of choices. The term "multi-armed bandit" comes from a hypothetical experiment where a person must choose between multiple actions (i.e., slot machines, the "one-armed bandits"), each with an unknown payout. In marketing terms, a multi-armed bandit solution is a ‘smarter’ or more complex version of A/B testing that uses machine learning algorithms to dynamically allocate traffic to variations that are performing well, while allocating less traffic to variations that are underperforming.
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